A qualitative and quantitative synthesis of the impacts of COVID-19 on soundscapes: A systematic review and meta-analysis

Abstract

The current prolonged coronavirus disease (COVID-19) pandemic has substantially influenced numerous facets of our daily lives for over two years. Although a number of studies have explored the pandemic impacts on soundscapes worldwide, their works have not been reviewed comprehensively nor systematically, hence a lack of prospective soundscape goals based upon global evidence. This review study examines evidence of the COVID-19 crisis impacts on soundscapes and quantifies the prevalence of unprecedented changes in acoustic environments. Two key-research classes were identified based on a systematic content analysis of the 119 included studies: (1) auditory perceptual change and (2) noise level change due to the COVID-19 pandemic/lockdown. Our qualitative synthesis ascertained the substantial adverse consequences of pandemic soundscapes on human health and well-being while beneficial aspects of the COVID-19 pandemic on soundscapes were yet identified. Furthermore, meta-analysis results highlight that the observed average noise-level reduction (148 averaged samples derived from 31 studies) varied as a function of the stringency level of the COVID-19 confinement policies imposed by the governments, which would be further moderated by urban morphology and main noise sources. Given these collective findings, we propose soundscape materiality, its nexus with related the United Nations' sustainable development goals (SDGs), and prospective approaches to support resilient soundscapes during and after the pandemic, which should be achieved to enhance healthy living and human well-being.

Keywords: Acoustic environment; COVID-19; Environmental health; Human well-being; Resilient soundscape; Sustainable development goal.

Graphical abstract

Fig. 1

The term co-occurrence network of papers devoted to soundscape, acoustic/noise environment, and COVID-19. This term map visualizes the terms that appear in the titles and abstracts of articles already published as literature. The large bubbles indicate which terms are mentioned more frequently. The terms that are closer to each other and appear the same color are terms that co-occur more frequently. Under the total 3394 terms identified automatically, terms with fewer than 9 occurrences were removed (i.e., minimum number of occurrences of a term: 10). Generic terms (e.g., day, year, study, time) were manually removed by the authors, resulting in 41 items: 23 and 18 items for the first (red) and second (green) classes.

Fig. 2

Geographical distribution of the research investigations. Circle spots roughly identify states of provenances where the included studies have been conducted or their responses were collected. Size of marks represents the number of studies analyzing data from individual countries. Color of marks represents geographical groups. Map generated using Microsoft BI.

Fig. 3

Comprehensive reflects of qualitative synthesis of the collected studies in the two research classes. This figure highlights important facets of negative soundscape changes due to the COVID-19 pandemic and their adverse consequences. WFH = Work from home; LFH = Learn from home; EJC = Environmental justice community. n_i = Number of pandemic-related impacts on soundscapes.

Fig. 4

n_sample = 148. Average noise reduction (dB or dBA) as a function of strigency index score (Oxford University, 2022). It shows the trends in the common acoustical parameters, denoted as regression lines. Each symbol represents a sample mean of the noise level reduction identified and/or calculated from the 31 individual studies. A monotonic reduction in 24-h continuous (all-time) (L_{eq, 24h}, L_den, L_dn), daytime (L_d: 6:00–18:00), and evening-time (L_e: 18:00–22:00) levels by increasing severity levels of the COVID-19 confinements from pre to during the COVID-19 periods, while night-time level (L_n: 22:00–6:00) reductions were less apparent. The slopes of the regression lines and their statistical significance can be found in Table 2.

Fig. 5

n = 31; n_s = 148. Average noise level change (dB or dBA) as a function of various urban morphologies from the 31 studies in the aggregate analysis, and the error bars indicate standard deviations. The studied locations were categorized into three main areas (active, quiet, and traffic-dominated areas) in accordance with their functional and spatial characteristics (referred to Asensio et al., 2020b). Mean noise reduction varied across the areas: bars represent individual locations and point symbols represent the three main areas. Greatest noise reduction was found in the active area (especially in night-life area). Examples of these locations include commercial, restaurant, tourist destination, and industrial areas (active area), residential and park areas (quiet area), road intersection and highway (traffic-dominated area). All example images are free for use, no attribution is required.

Fig. 6

A framework of prospective soundscape approahces during and after the COVID-19 pandemic. Three soundscape materiality concepts and their nexuses with five SDGs focus areas (left) are outlined to describe the proposed framework (right), which subsequenltly helps to establish resilient soundscapes.