The noise footprint is emerging as a framework for assessing and communicating the impacts of noise. Although footprint terminology has been used across diverse areas of environmental acoustics research, its meaning, definition and application vary across fields. This research aims to provide a consensus-based noise footprint definition, identify the conceptual and methodological requirements that should guide future quantitative implementation and establish its basic operational characteristics. An adjusted Delphi process was conducted with a panel of 8 experts spanning two domains, namely acoustics and Life Cycle Assessment & environmental footprinting. The process combined semi-structured interviews and two rounds of questionnaires to establish consensus on the conceptual definition and quantitative characteristics of the noise footprint and thematic analysis of the collected material. The outcomes were subsequently synthesized through a SWOT analysis to identify strengths, weaknesses, opportunities, and threats associated with the proposed framework. In contrast to most existing noise footprint applications, the proposed framework suggests a life cycle perspective and supply chain attribution. The noise footprint is proposed, on the basis of expert consensus, as a measure for assessing and comparing noise, applicable to products and adaptable to individual and societal activities. It is best understood as a structured indicator with two levels of resolution: an emission-based level inventorying the sound energy of interconnected sources, and an impact-based level that incorporates exposure of affected receptors. The framework supports the tracing of acoustic pressures across systems, enabling the identification of impacts and shared responsibilities in noise management.

OBJECTIVE:Transportation noise is a pervasive environmental pollutant that has significant implications for physical and mental health, particularly in urban areas. This scoping review summarizes the long-term health effects of transport noise from road, rail, and aircraft sources and evaluates available mitigation strategies in Europe. METHODS:Using the Reporting Standards for Systematic Evidence Syntheses (ROSES), we identified 74 peer-reviewed articles from Scopus, Web of Science, and Google Scholar in order to assess health impacts of noise and the available strategies for mitigating them. RESULTS:Most studies encompassed European regions via multicountry datasets. The findings support the well-known impacts of noise on human health, including cardiovascular disease, increased blood pressure, annoyance, sleep disturbance, and evidence of adverse mental performance and cognitive impairment outcomes related to noise. Vulnerable populations, such as children and the elderly, are disproportionately affected. Mitigation strategies are subdivided into categories of source, propagation, and exposure. Propagation strategies, such as acoustic barriers and vegetation belts report 8-12 A-weighted decibel (dB(A)) and 2.5-8.5 dB(A) reduction, while source reductions of traffic optimization (1-3.5 dB(A)) and calming (1-5 dB(A)) are lower. Mitigation strategies addressing physical effects primarily focus on source, propagation, and planning measures, whereas strategies targeting mental effects emphasize exposure reduction, such as soundproofing and improvements to the immediate environment through greenery. CONCLUSIONS:Gaps remain in understanding the cumulative effects of multimodal noise exposure, gender-specific responses, and the integration of objective health measures. The scoping review highlights the potential and urgent need for integrated, proactive noise mitigation strategies in spatial and urban planning.

BACKGROUND:Long-standing research on the relationship between the urban acoustic environment (AE) and human health demonstrates the harmful effects of environmental noise. Meanwhile, an increasing number of smaller studies report health benefits for additional acoustic properties. However, studies on health-promoting AEs remain limited, largely due to the lack of methods for estimating high-resolution acoustic properties beyond conventional noise metrics. OBJECTIVE:We investigate to what extent models based on land-use types (LUT) can predict urban AE properties, focusing on four acoustic indices (Articulation Index, Bioacoustic Index, Link Density and Sharpness). Additionally, we predict the LAeq, which enables us to compare the performance between our model, the strategic noise map of Bochum (SNM) and results from the literature. METHODS:We use a dataset of 2,746 acoustic measurements from 785 locations in Bochum and 90 measurements from 22 locations in Essen to train and evaluate gradient boosting models. For model development, data is split into training/validation (668 locations in Bochum) and test sets (117 locations in Bochum, all locations in Essen). The models predict acoustic indices based on the area of 77 LUTs within 50 and 300 m buffers around each location. RESULTS:Based on the root mean square error (RMSE), predictions for Link Density deviate on average by 0.17 and 0.21 from test-sets in Bochum and Essen. For LAeq, the RMSE is 4.8 dB(A) and 4.4 dB(A), respectively. The R2 for Link Density is between 0.27 and 0.3, and for the LAeq between 0.52 and 0.46. The SNM performs worse in predicting LAeq for Bochum data (RMSE = 7.8 dB(A); R2 = -0.31). Performances for other indices are mixed. IMPACT:This study advances research on the urban acoustic environment by demonstrating that land use type-based models represent a promising approach to predict acoustic indices beyond conventional noise metrics. Using over 2,800 measurements from two German cities, the models for predicting the Link Density and the LAeq show moderate to good performance on two test datasets. Model predictions for the LAeq outperformed strategic noise maps in predicting total environmental noise. These findings open new pathways for large-scale, population-based health research by providing a promising, scalable, high-resolution method for characterising complex urban acoustic environments, supporting efforts to design healthier urban environments through higher acoustic quality. SIGNIFICANCE:LUT-based models demonstrate their potential for predicting Link Density and LAeq, achieving moderate to strong performance across two independent test datasets. This can provide a scalable approach for investigating potentially health-relevant properties of the urban AE at high spatial resolution.

Urbanization has intensified the complexity of acoustic environments, necessitating a more comprehensive understanding to support urban acoustic planning for healthier living spaces. Traditional noise monitoring, primarily based on sound pressure level indices, is insufficient for capturing the full scope of these environments. This study investigates whether a diverse set of acoustic metrics can improve the characterization of acoustic environments and examines their stability across different land use types. We analyzed 1 year of time-series data from acoustic monitoring stations in Bochum, Germany, calculating psychoacoustic, ecoacoustic, and complex network indices. Our goals were to: (1) identify interdependencies among selected metrics, (2) uncover temporal patterns in acoustic measurements, and (3) relate them to their respective locations. Methods included correlation analysis, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering, principal component analysis, and descriptive statistics with diurnal aggregation. The findings demonstrate that acoustic indices of eight distinct dimensions, along with eight individual metrics, reveal crucial temporal, spatial variations of the acoustic environment and the interplay of individual sound sources overlooked by conventional sound pressure level (SPL) metrics. In particular, the study identifies the maximum Sharpness (Aures method), Link Density, the Bioacoustic Index, and the Amplitude Index as the most effective predictors of land use types, achieving the highest Adjusted Rand Index values (0.25, 0.17, 0.13, 0.13). Incorporating such indices into acoustic monitoring practices offers a refined, site-sensitive framework to identify more nuanced qualities of the acoustic environment, therefore, potentially laying the groundwork for targeted urban interventions that could promote health.

Urban regions represent complex acoustic environments with few respites from noise other than small or remote patches of green infrastructure (GI). Recent noise action planning in the German Ruhr region indicates that urban expansion is fueling encroachment upon GI and subsequently the loss of quiet areas. A systematic exploration of this loss in Germany is needed. An explorative systematic review on Scopus with snowballing supports the synthesis of a conceptual framework linking acoustically relevant ecosystem services with GI. Our review identifies natural quietness, abatement, connection to nature, positive soundscape perception, fidelity, and bird sound presence as sound-related ecosystem functions or services. Empirical case studies justify the need to better understand the link between GI, ecosystem services, and the acoustic environment. Guidance for quiet area assessments in the EU to address this research gap in noise action planning is an emerging topic and needs further study. To address the knowledge gap and provide quiet area assessment guidance, we present a stratified habitat-based acoustic study design for a multi-community area in the middle of the German Ruhr region. A multi-tier sample of 120 locations across eleven habitat and land use strata in the Ruhr is presented, pointing out the scarcity of protected biotopes and large biotope complexes in the study area. This work is a contribution towards a conceptual and methodological basis for quiet area assessment, especially in German and EU noise action planning.

Introduction: Urban areas in Europe face noise pollution challenges. The European Environmental Noise Directive with noise mapping and noise planning instruments aim to alleviate the negative physical and mental health outcome from noise. As noise action plans are in their 4th iteration across Germany, the integration of quiet areas in noise action planning is emerging. This paper investigates the relationship between noise annoyance, active mobility, and quiet area planning, focusing on integration of quiet routes into noise action plans to effectively extend the effects of and access to quiet areas and active mobility. Methods: The aim of the study is to enhance the accessibility of quiet areas via a novel quiet routes concept, and assess quiet area accessibility in Dortmund. A least-cost-path model optimized for quiet routes via a multi-factor cost surface delineates a quiet route network connecting noiseannoyed residents to quiet areas in Dortmund, Germany. Results: Findings indicate that 15.8 % and 37.8 % of noise-annoyed population have walkable access to quiet areas within 350 m and 700 m respectively (5-10-min walk) and 97.7 % have cycling access within 4500 m (15-min bike ride). The energetic mean of LDEN is only 1.09 dB(A) less than on shortest routes, but a mode difference of 5.62 dB(A) underpins an increase in quiet segments. Strahler ordering indicates that 37.6 % of the noise-annoyed population could be served via 4th and 5th order segements, just 3.24 % of the quiet route network. Conclusions: Integrating quiet routes into noise action plans expands the functional reach of quiet areas, providing a new quiet area strategy. The proposed hierarchical classification system provides a practical tool for prioritizing strategic infrastructure improvements, ensuring costeffective interventions with a measurable impact. This framework offers a scalable planning approach applicable to other urban contexts seeking to improve noise exposure conditions for active mobility users.

The beneficial effects of urban greenspace on human health and well-being are widely recognized, but the underlying mechanisms are incompletely understood. The acoustic environment (AE) is often proposed as a detrimental factor and emerging evidence suggests that it may also represent a beneficial resource. If different dimensions of the AE were to represent a mediator between greenspace and human health, they would need to be associated with both. We present results investigating the first prerequisite: the associations between acoustic dimensions and urban greenspace. We use audio recordings from SALVE, randomly sampled at 730 locations four times per year (n=2,746) in Bochum, Germany. The AE is quantified by 122 eco- and psychoacoustic indices, sound pressure levels and complex network measures. Greenspace is defined as the percentage of green area in a 300 m-buffer around each recording location, grouped into quintiles. We apply dimensionality reduction and present descriptive statistics to analyze the associations between identified acoustic dimensions and urban green area. In addition to the "sound volume", we find that biophonic sounds, the Acoustic Richness, the Impulsiveness, the Roughness, and the "acoustic dominance" show a consistently decreasing/increasing trend across the five quintiles of urban green area.

Soundscapes have been studied by researchers from various disciplines, each with different perspectives, approaches, and terminologies. Consequently, the research field determines the actual concept of a specific soundscape with the associated components and also affects the definition itself. This complicates interdisciplinary communication and comparison of results, especially when research areas are involved which are not directly focused on soundscapes. For this reason, we present a formalization that aims to be independent of the concepts from the various disciplines, with the goal of being able to capture the heterogeneous data structure in one layered model. Our model consists of time-dependent sound sources and geodata that influence the acoustic composition of a soundscape represented by our sensor function. Using a case study, we present the application of our formalization by classifying land use types. For this we analyze soundscapes in the form of recordings from different devices at 23 different locations using three-dimensional convolutional neural networks and frequency correlation matrices. In our results, we present that soundscapes can be grouped into classes, but the given land use categories do not have to correspond to them.

The association of urban greenspace and human health and well-being is widely recognised, but the underlying mechanisms are incompletely understood. The acoustic environment (AE) is frequently proposed as a mediator between greenspace and human health. While it is commonly viewed as a negative health factor (e.g. noise pollution), there is growing evidence that it also has positive effects on human health. However, a general problem is the lack of information on the AE for greenspaces in high spatial resolution. To provide evidence-based support for research on this issue, we identify and assess acoustic properties of health-related urban greenspace by estimating the association between urban green area and selected acoustic indices.The analysis is based on 5-minute audio recordings collected systematically from 730 locations, four times a year (n = 2,746) in Bochum, Germany. To quantify the acoustic properties, we use an indicator-set of 30 sound pressure levels, 11 eco-, 78 psychoacoustic indices and 3 complex network measures. Greenspace is defined, in alignment with various health studies, as the percentage of green area within three buffer sizes (50, 100, 300 m) around each recording location.Through cluster analysis, we identify eleven acoustic dimensions in our indicator-set, for which representative indices are derived using principal component analysis. Descriptive statistics and multi-level regression models are used to analyse the associations between representative indices and greenspace. We find the highest effect sizes for Link Density (representing “acoustic dominance”), Acoustic Richness (representing “persistent sound volume”) and the Articulation Index (representing “sound volume”). Thus, in addition to “sound volume”, the AE of urban greenspace is characterised to a similar extent by at least two additional acoustic dimensions. If properties of the AE act as mediators between urban greenspace and human health, these three acoustic dimensions hold the potential to reflect them. Studies on pathways between greenspace and human health could incorporate measures of these dimensions to extend the understanding of the AE’s relevance for Public Health.

German noise action plans aim to reduce negative health outcomes from noise exposure and identify quiet areas free of noise pollution. Quiet area identification in German noise action plans is based primarily on noise mapping and spatial analysis and not empirical or qualitative data about acoustic environments, thus leaving a gap in the understanding of the quality of formally recognized quiet areas in noise action plans. This work presents a comparative empirical case study in Dortmund, Germany, with the aim to describe the diurnal dB(A) and biophonic properties of quiet areas versus noise ‘hot spots’. Sound observations (n = 282,764) were collected in five different natural or recreational land use patch types larger than four acres within 33 proposed quiet areas in Dortmund (n = 70) and 23 noise hot spots between 27 April 2022 and 2 March 2023. We found that quiet areas are on average more than 20 dB(A) quieter than noise hot spots almost every hour of the day. Forests, managed tree stands, cemeteries, and agriculture diel patterns are dominated by dawn dusk chorus in spring and summer, whereas sports and recreation as well as noise hot spots are dominated by traffic and human noise. A novel composite biophony mapping procedure is presented that finds distinct temporal distribution of biophony in forested and agriculture peri-urban locations positively associated with patch size, distance away from LDEN > 55, proximity to water, and the number of vegetation layers in the plant community. Anthrophony distribution dominates urban land uses in all hours of the day but expands during the day and evening and contracts at night and in dusk hours. The procedures presented here illustrate how qualitative information regarding quiet areas can be integrated into German noise action planning.

The urban acoustic environment (AE) contains valuable information on complex sub-systems of urban areas, such as traffic or biodiversity.As the availability of cheap sensors and computational power increases, so does the need for methods to process high-dimensional audio data.We take this as an opportunity to introduce complex networks (CNs) to the field of urban acoustics.CNs have proved effective in capturing the complexity of e.g.climate dynamics or brain structures, and thus, represent a promising tool for the high-dimensional urban AE.We present how CNs are constructed based on frequency correlation matrices and show their behavior for various time periods and sound sources.To demonstrate their use on audio data from the urban environment, we apply them to the dataset from the SALVE study to systematically characterize the urban AE.Here, we use subsets of SALVE with two different temporal resolutions: 1 s over three minutes and three minutes over 24 h.Measures such as the average shortest path length identify urban sites with similar AEs, indicating the utility of CNs to identify non-random patterns in large datasets of the urban AE.

During the SARS-CoV-2 pandemic, sound pressure levels (SPL) decreased because of lockdown measures all over the world. This study aims to describe SPL changes over varying lockdown measure timeframes and estimate the role of traffic on SPL variations. To account for different COVID-19 lockdown measures, the timeframe during the pandemic was segmented into four phases. To analyze the association between a-weighted decibels (dB(A)) and lockdown phases relative to the pre-lockdown timeframe, we calculated a linear mixed model, using 36,710 h of recording time. Regression coefficients depicting SPL changes were compared, while the model was subsequently adjusted for wind speed, rainfall, and traffic volume. The relative adjusted reduction of during pandemic phases to pre-pandemic levels ranged from −0.99 dB(A) (CI: −1.45; −0.53) to −0.25 dB(A) (CI: −0.96; 0.46). After controlling for traffic volume, we observed little to no reduction (−0.16 dB(A) (CI: −0.77; 0.45)) and even an increase of 0.75 dB(A) (CI: 0.18; 1.31) during the different lockdown phases. These results showcase the major role of traffic regarding the observed reduction. The findings can be useful in assessing measures to decrease noise pollution for necessary future population-based prevention.

The urban acoustic environment (AE) provides comprehensive acoustic information related to the diverse systems of urban areas, such as traffic, the built environment, or biodiversity. The decreasing cost of acoustic sensors and rapid growth of storage space and computational power have fostered the collection of large amounts of acoustical data to be processed. However, despite the extensive information that is recorded by modern acoustic sensors, few approaches are established to capture the rich complex dynamics embedded in the time-frequency domain of the urban AE. Quantitative methods need to account for this complexity, while effectively reducing the high dimensionality of acoustic features within the data. Therefore, we introduce complex networks as a tool for analyzing the complex structure of large-scale urban AE data. We present a framework to construct networks based on frequency correlation matrices (FCMs). FCMs have shown to be a promising tool to depict environment specific interrelationships between consecutive power spectra. Accordingly, we show the capabilities of complex networks for the quantification of these interrelationships and thus, to characterize different urban AEs. We demonstrate the scope of the proposed method, using one of the world's most extensive longitudinal audio datasets, considering 3-min audio recordings (n = 319,385 ≙ 665 days) from 23 sites. We construct networks from hour-of-day specific audio recordings for each site. We show that the average shortest path length (ASPL) as an indicator for dominance of sound sources in the urban AE exhibits spatial- and temporal-specific patterns between the sites, which allows us to identify four to seven clusters of distinct urban AEs. To validate our findings, we use the land use mix around each site as a proxy for the AE and compare those between and within the clusters. The identified clusters show high intra- and low inter-cluster correlations of ASPL diel cycles as well as strong intra-similarities in land use mix. Our results indicate that complex networks are a promising approach to analyze large-scale audio data, expanding our understanding of the time-frequency domain of the urban AE.

Soundscapes have been studied by researchers from various disciplines, each with different perspectives, goals, approaches, and terminologies. Accordingly, depending on the field, the concept of a soundscape's components changes, consequently changing the basic definition. This results in complicating interdisciplinary communication and comparison of results. Especially when soundscape-unrelated research areas are involved. For this reason, we present a potential formalization that is independent of the underlying soundscape definition, with the goal of being able to capture the heterogeneous structure of the data as well as the different ideologies in one model. In an exemplary analysis of frequency correlation matrices for land use type detection as an alternative to features like MFCCs, we show a practical application of our presented formalization.

Research about human perception of the acoustic environment is dominated by the use of sound pressure levels (SPL) or proprietary psychoacoustic indicators, limiting the link between sound and perception to only these two approaches. The aim of this study is to make connections between soundscape perception descriptors and ecoacoustic indices so that a perceptual dimension of ecoacoustic indices is ascertained. The study employs a laboratory experiment where 309 participants were exposed to sound and imagery from nine different urban land uses (n = 2,781) according to the soundscape protocol DIN ISO 12913-2. Spearman’s correlation and ordinal logistic regression are used to analyze the relationship between continuous numeric ecoacoustic indices and Likert scale psychoacoustic perception outcomes. We find that the predictors median amplitude (M), acoustic richness (AR), number of peaks (NP), and acoustic evenness index (AEI) have significant correlations (r > 0.5: p ≤ 0.05) and effect sizes (β ≥ 0.1) for the psychoacoustic outcomes perceived affective quality (PAQ) of calm, pleasant, chaotic, annoying, and soundscape identifiers (SSI) traffic sounds and natural sounds. We find that between 59.4 % and 81.9 % of the variation within dependent SSI and PAQ variables is explained by ecoacoustic indices based on Nagelkerke’s R-Squared values. High M and AR values are indicators for sound environments with high median or consistent amplitude, such as traffic noise, and high NP and AEI values are indicators for high fidelity sound environments with natural sounds or human beings that may be perceived as calm or pleasant. We conclude that combinations of M, AR, NP, and AEI as composite indicators contribute to understanding nine of the 14 psychoacoustic perception outcomes in DIN ISO 12913-2.

Average temperatures continue to rise throughout the world due to climate change and, thus, also in Europe, often occurring as heat waves. The negative effects of climate change-related heat waves can be observed, especially in urban areas where land sealing is the greatest and so is population density. Past studies have indicated that green volume can provide climate improvement by balancing humidity and regulating temperature. This study aims to estimate the distribution of surface heat islands and green volume and test the relationship between these variables in a case study of Bochum, Germany. A method to develop a temporally longitudinal 30-m Landsat 8-based land surface temperature (LST) analysis and 30-m LiDAR-based green volume dataset are presented, and their relationship is tested using Pearson’s correlation (n = 148,204). The results show that heat islands are moderately negatively correlated with green volume (r = −0.482; p < 0.05), LST can vary as much as 28 degrees °C between heat islands and densely vegetated areas, and the distribution is heterogeneous across Bochum.

(Prof. Dr. rer. nat. Susanne Moebus, Center for Urba n Epidemiology (CUE), Institute for Medical Informat ics Biometry and Epidemiology University Colonia Haus Room 3.16 Zweige rstr. 37 45130 Essen Germany, susanne.moebus@uk-ess en.de) (Dr. rer. medic Robynne Sutcliffe, Center for Urban E pidemiology (CUE), Institute for Medical Informatics Biometry and Epidemiology University Colonia Haus Room 3.16 Zweige rstr. 37 45130 Essen Germany, robynne.sutcliffe@ukessen.de) (M.Sc. Salman Ahmed, Center for Urban Epidemiology ( CUE), Institute for Medical Informatics Biometry and Epidemiology University Colonia Haus Room 3.16 Zweigerstr. 37 451 30 Essen Germany, salman.ahmed@uk-essen.de) (Dr.-lng. Bryce Lawrence, Department of Landscape Ec ology and Landscape Planning, TU Dortmund Universit y School of Spatial Planning Campus South GB III Room 3.320 August-Schmidt -Str. 10 44227 Dortmund Germany, bryce.lawrence@udo .edu) (B.A. Timo Haselhoff, Center for Urban Epidemiology ( CUE), Institute for Medical Informatics Biometry and Epidemiology University Colonia Haus Room 3.16 Zweigerstr. 37 451 30 Essen Germany, timo.haselhoff@uk-essen.de) (Prof. Dr. -lng. Dietwald Gruehn, Department of Lan dscape Ecology and Landscape Planning, TU Dortmund University School of Spatial Planning Campus South GB III Room 3.320 August -Schmidt-Str. 10 44227 Dortmund Germany, dietwald.g ruehn@udo.edu)

Sound pressure levels (SPL) in dB are the traditional noise measurement, but dB measures alone limit characterization of the complex urban acoustic environment. The aim of this paper is to apply alpha and beta ecoacoustic indices from soundscape ecology at two contrasting locations corresponding to an urban mixed-use corridor and a deciduous forest. Twenty acoustic indices and mean dB(A) are calculated from 2,798 three-minute recordings taken every 26 min over a 28-day period. Acoustic indices are summarized by case with mean hour-of-day plots and 7- and 28-day time series plots to identify temporal patterns. Correlations to assess index relationships for each case separately and combined are presented. Acoustic indices provide complimentary information beyond SPL measures alone, especially in frequency and temporal domains, useful to identify patterns in the acoustic environment. Correlation between specific multiple indices at individual locations is a promising approach to identification of anthrophonic and biophonic dominated sonotopes. (C) 2021 The Authors. Published by Elsevier Ltd.

As sustainable metropolitan regions require more densely built-up areas, a comprehensive understanding of the urban acoustic environment (AE) is needed. However, comprehensive datasets of the urban AE and well-established research methods for the AE are scarce. Datasets of audio recordings tend to be large and require a lot of storage space as well as computationally expensive analyses. Thus, knowledge about the long-term urban AE is limited. In recent years, however, these limitations have been steadily overcome, allowing a more comprehensive analysis of the urban AE. In this respect, the objective of this work is to contribute to a better understanding of the time–frequency domain of the urban AE, analysing automatic audio recordings from nine urban settings over ten months. We compute median power spectra as well as normalised spectrograms for all settings. Additionally, we demonstrate the use of frequency correlation matrices (FCMs) as a novel approach to access large audio datasets. Our results show site-dependent patterns in frequency dynamics. Normalised spectrograms reveal that frequency bins with low power hold relevant information and that the AE changes considerably over a year. We demonstrate that this information can be captured by using FCMs, which also unravel communities of interlinked frequency dynamics for all settings.