How did the 'state of emergency' declaration in Japan due to the COVID-19 pandemic affect the acoustic environment in a rather quiet residential area?

The Role of Traffic Volume on Sound Pressure Level Reduction before and during COVID-19 Lockdown Measures-A Case Study in Bochum, Germany

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.

Using a Clustering Method to Detect Spatial Events in a Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment

Noise has become a very notable source of pollution with major impacts on health, especially in urban areas. To reduce these impacts, proper evaluation of noise is very important, for example by using noise mapping tools. The Noise-Planet project seeks to develop such tools in an open science platform, with a key open-source smartphone tool "NoiseCapture" that allows users to measure and share the noise environment as an alternative to classical methods, such as simulation tools and noise observatories, which have limitations. As an alternative solution, smartphones can be used to create a low-cost network of sensors to collect the necessary data to generate a noise map. Nevertheless, this data may suffer from problems, such as a lack of calibration or a bad location, which lowers its quality. Therefore, quality control is very crucial to enhance the data analysis and the relevance of the noise maps. Most quality control methods require a reference database to train the models. In the context of NC, this reference data can be produced during specifically organized events (NC party), during which contributors are specifically trained to collect measurements. Nevertheless, these data are not sufficient in number to create a big enough reference database, and it is still necessary to complete them. Other communities around the world use NC, and one may want to integrate the data they collected into the learning database. In order to achieve this, one must detect these data within the mass of available data. As these events are generally characterized by a higher density of measurements in space and time, in this paper we propose to apply a classical clustering method, called DBSCAN, to identify them in the NC database. We first tested this method on the existing NC party, then applied it on a global scale. Depending on the DBSCAN parameters, many clusters are thus detected, with different typologies.

Investigating urban soundscapes of the COVID-19 lockdown: A predictive soundscape modeling approach

The unprecedented lockdowns resulting from COVID-19 in spring 2020 triggered changes in human activities in public spaces. A predictive modeling approach was developed to characterize the changes in the perception of the sound environment when people could not be surveyed. Building on a database of soundscape questionnaires (N = 1,136) and binaural recordings (N = 687) collected in 13 locations across London and Venice during 2019, new recordings (N = 571) were made in the same locations during the 2020 lockdowns. Using these 30-s-long recordings, linear multilevel models were developed to predict the soundscape pleasantness ( R2=0.85) and eventfulness ( R2=0.715) during the lockdown and compare the changes for each location. The performance was above average for comparable models. An online listening study also investigated the change in the sound sources within the spaces. Results indicate (1) human sounds were less dominant and natural sounds more dominant across all locations; (2) contextual information is important for predicting pleasantness but not for eventfulness; (3) perception shifted toward less eventful soundscapes and to more pleasant soundscapes for previously traffic-dominated locations but not for human- and natural-dominated locations. This study demonstrates the usefulness of predictive modeling and the importance of considering contextual information when discussing the impact of sound level reductions on the soundscape.

Indoor soundscapes at home during the COVID-19 lockdown in London - Part I: Associations between the perception of the acoustic environment, occupantś activity and well-being

Since the outbreak of the COVID-19 pandemic, as a result of the adoption of worldwide lockdown measures, the home environment has become the place where all the daily activities are taking place for many people. In these changed social and acoustical contexts, we wanted to evaluate the perception of the indoor acoustic environment in relation to traditional and new activities performed at home, i.e., relaxation, and working from home (WFH). Taking London as a case study, the present paper presents the results of an online survey administered to 464 home workers in January 2021. The survey utilized a previously developed model for the assessment of indoor soundscapes to describe the affective responses to the acoustic environments in a perceptual space defined by comfort (i.e. how comfortable or annoying the environment was judged) and content (i.e., how saturated the environment is with events and sounds) dimensions. A mixed-method approach was adopted to reinforce result validity by triangulating data from questionnaires and spontaneous descriptions given by participants. In this first part of the study, the main objectives were: (1) evaluating differences in soundscape evaluation, in terms of comfort and content dimensions, based on the activity performed at home, (2) identifying appropriate conditions for WFH and relaxation, and (3) investigating associations between psychological well-being and indoor soundscapes. The results showed that the environments were perceived as more comfortable and slightly fuller of content when rated in relation to relaxation than for WFH, thus suggesting a stricter evaluation of the acoustic environment in the latter case. As regards the second objective, spaces that were more appropriate for relaxation had high comfort, whereas spaces appropriate for WFH resulted more private and under control, i.e. with high comfort and low content scores. Lastly, better psychological well-being was associated with more comfortable soundscapes, both for WFH (rs = 0.346, p < .0005), and relaxation (rs = 0.353, p < .0005), and with lower content while WFH (rs = -0.133, p = .004). The discussion points out the need of considering the implications of changed working patterns to rethink the design of soundscapes in residential buildings, also in relation to potential well-being outcomes that will be further investigated in the Part II of the study.

A Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment

Noise is a major source of pollution with a strong impact on health. Noise assessment is therefore a very important issue to reduce its impact on humans. To overcome the limitations of the classical method of noise assessment (such as simulation tools or noise observatories), alternative approaches have been developed, among which is collaborative noise measurement via a smartphone. Following this approach, the NoiseCapture application was proposed, in an open science framework, providing free access to a considerable amount of information and offering interesting perspectives of spatial and temporal noise analysis for the scientific community. After more than 3 years of operation, the amount of collected data is considerable. Its exploitation for a sound environment analysis, however, requires one to consider the intrinsic limits of each collected information, defined, for example, by the very nature of the data, the measurement protocol, the technical performance of the smartphone, the absence of calibration, the presence of anomalies in the collected data, etc. The purpose of this article is thus to provide enough information, in terms of quality, consistency, and completeness of the data, so that everyone can exploit the database, in full control.

A Note on Variation of the Acoustic Environment in a Quiet Residential Area in Kobe (Japan): Seasonal Changes in Noise Levels Including COVID-Related Variation

This communication compares the previously reported results of the acoustic environment, mainly noise levels at a fixed point, in a quiet residential area in Kobe, Japan, under the declaration of the COVID-19 state of emergency in May 2020 with the results of two follow-up studies in the same area: subsequent follow-up noise measurements in June and July–August 2020, and the present results of measurements in September–October 2020. The results of the comparison among the above three measurements suggest that noise levels were lower during September-October 2020 than during the declaration of the state of emergency in May 2020. In the period from May to October 2020, the noise level was significantly higher in July and August of the same year due to the sound of cicadas, which are common in this area. This suggests that it is difficult to set the target values of the acoustic environment planning by referring to the low noise level at lockdown or similar measures in areas with large seasonal variations in acoustic environment. Although many case studies are necessary to obtain appropriate target values, one case study is presented in this communication to illustrate an example and discuss its difficulty.