Sound complexity as a strategy for livable and sustainable cities: The case of an urban waterfront
-
Aggelos Tsaligopoulos
,
Stella Sofia Kyvelou
Abstract
Public spaces underwent a notable shift in their acoustic profile during the societal restrictions resulting from the COVID-19 pandemic. A silver lining emerged from this global crisis, with noticeable improvements in public acoustic environments due to reduced noise levels resulting from restricted mobility. This research focuses on the acoustic environment of Thessaloniki’s waterfront, a Mediterranean metropolis in Northern Greece. Waterfronts of Mediterranean coastal cities provide unique acoustic environments worthy of protection from environmental noise. By analyzing sound level measurements and recordings during the 2021 lockdown and comparing them to the post-lockdown period in 2022, we aimed to explore environmental noise and acoustic complexity indicators. The study’s findings revealed a significant increase in acoustic complexity during the lockdown, underscoring an inverse relationship between noise levels and acoustic complexity. Urban waterfronts, like Thessaloniki’s, hold great potential for enhancing acoustic complexity and subsequently improving the acoustic quality of public spaces while protecting them from environmental noise. This research sheds light on the possible use of sound complexity as an environmental quality standard that can be incorporated in sustainable urban planning and design.
1 Introduction
The Mediterranean coastal and port cities present distinct profiles [1] and sonic identities, offering a unique and diverse sensorial experience [2]. Waterfronts in coastal cities described as the interface of urban space and sea [3] hold high restorative, recreational, ecological, cultural, and aesthetic values [4,5]. During the COVID-19 pandemic, waterfronts transformed into a primary choice for exercise [6,7] and dog walks [8] during the day time. The waterfront studied in this research stands as a landmark of the city of Thessaloniki (Central Macedonia, Greece) and offers a highly restorative promenade for locals and tourists.
The COVID-19 pandemic had a considerable multi-level impact on city life [9] as lockdowns and curfews were implemented from 2020 until May 2021. The lockdowns caused prolonged reductions in the number of vehicles on roads [10,11] and overall human movement, leading to significant alterations in the urban sound environment. Amongst the most noticeable alterations were the reduced noise levels in urban settings [12] that used to be dominated by noise. This observation highlights the importance of promoting healthier urban environments [13] while simultaneously giving attention to the acoustic environments of a city [14]. Consequently, researchers were motivated to research this profound situation from various perspectives including outdoor [15] and indoor noise changes [16].
Mitchel et al. [17] pointed out that, during the lockdown period, there was a shift in people’s perception regarding urban soundscapes. The phenomenological approach [18] through which individuals interpret and understand the sounds around them is reinforced by the objective reality of the natural world. The change in perception occurred because city birds recovered their song space, which had been taken over by the sound of traffic noise [19]. To this end, a criterion that signifies a healthy acoustic environment is the degree to which organisms can vocally interact without being masked by noise [20]. Natural acoustic patterns can be overshadowed, resulting in acoustic environments of reduced acoustic complexity [21,22]. The implemented lockdowns removed a sonic layer and liberated the frequencies that were dominated by human acoustic activity, thus lowering the levels of background noise [23]. Therefore, a new acoustic environment with distinct spectral properties and ecological essence is derived.
1.1 Motive and scope of research
Cities are complex and dynamic systems, wherein changes in one area can lead to unforeseen consequences in others. They must be able to adapt to varying circumstances, including environmental stressors similar to noise. Hence, nurturing complexity and adaptiveness in cities and in public urban areas similar to waterfronts plays a significant role in promoting urban health [24]. The United Nations’ 11th sustainable development goal [25] emphasizes the importance of inclusive, safe, resilient, and sustainable cities, recognizing complexity as a vital aspect of urban health and urban biodiversity [26]. Therefore, adopting a strategy that promotes complexity in urban planning and design can result in robust and adaptive systems [27,28,29].
Through this research, we study the effects of reduced human mobility, also called the “anthropause” [30], on the urban sound environment of a waterfront located in a Mediterranean metropolis. It is understood that enriched and complex acoustic environments are known to offer a series of auditory health-related benefits [31]. By examining the importance of the term “complexity” and by demonstrating its interplay with noise in two different human presence conditions, we aim to emphasize its role as an environmental quality standard in urban settings. In other words, sound complexity can stand as an assessment endpoint [32], which is defined as “an explicit expression of the environmental value to be protected” [33] from an environmental stressor similar to noise. Sound complexity, in this context, refers to an environmental trait that can be measured and could serve as a sustainable urban planning and design goal. The main objective of this research was to assess the impact of the lockdown period on the acoustic environment of the waterfront. Specifically, we examined changes in noise levels and acoustic complexity during and after the lockdown and investigated the inverse association between these two variables.
2 Background
2.1 The importance of complexity in ecological and acoustic systems
Complexity has been a subject of research for several years with an increasing trend. Researchers from several scientific disciplines focused on complexity from the scope of ecology [34] and acoustics in combination with soundscape studies [35,36]. The term complexity refers to a fabric of heterogeneous but associated elements [37]. The discipline of ecology describes complexity as an ecosystem feature that can be positively correlated with biological diversity [38]. Ecosystems are complex and display a non-linear dynamic in space and time, exhibiting a balance between regularity and chaos [34]. The biotic interactions, the energy and nutrient flows, demography, biogeography, and other natural processes, along with the factors or pressures that affect them, contribute towards the overall complexity of an ecosystem [38]. Ecological complexity is hard to precisely quantify but can be used as a measure of comparison, thus allowing the classification of ecosystems as of greater or lesser complexity. In contrast to simplicity, complexity is the result of ecological heterogeneity and of increased connection between the biotic and abiotic factors of an ecosystem [38,39,40]. Therefore, for the discipline of ecology, complexity is a valuable ecosystem trait that is highly correlated with ecological resilience, ecological integrity, and ecosystem health.
In regard to complexity in the acoustic environment or of a soundscape, it can be established by measurable acoustic elements that are present at a specific period of time. These elements can either be sonic events that can be detected, assessed, and used as soundscape descriptors by humans [41], or can be specific sound characteristics similar to signal amplitude. The latter characteristics are utilized by the ecoacoustics discipline [35,42] to compute the acoustic indicators that are used in rapid biodiversity assessments. Similar to complexity in ecology, acoustic complexity describes an aspect of biodiversity as a result of vocalization amplitude [43]. Acoustic complexity is measured through the Acoustic Complexity Index (ACI) that quantifies biotic sounds [44]. The specific indicator is based on the premise that biotic sounds present irregularities in amplitude in contrast to anthropogenic noise that is constant [45]. ACI is a relative metric, meaning that it is calculated based on the specific acoustic environment that is analyzed. Therefore, the ACI values obtained from two different areas are not directly comparable or additive. Instead, indices similar to ACI are used to compare the acoustic conditions of a specific area and highlight changes that occurred in time or after human intervention.
Moving away from the ecological meaning of sound to the perceptual construct of the acoustic environment, complexity is defined and used differently [46]. Mitchell et al. described the concept of soundscape complexity as the number of the sound sources in an acoustic environment [36]. An interesting outcome of this research was that a moderate complexity level is preferable in contrast to a simple or a chaotic soundscape.
Soundscapes, acoustic environments, and ecosystems present a degree of complexity at a temporal and spatial scale, ranging from the condition of simplicity or monotony to chaos. The constant, overlapping and disjoined components of an acoustic environment could result in an overcomplicated state with too much incoherent information, that can be described as homogeneous. Additionally, too many overlapping sound sources in an acoustic environment could result to an unpleasant soundscape [36]. Nevertheless, a chaotic soundscape could be unpleasant not only due to the overlapping sound sources but also due to the unrelatedness of its components. If the aural, visual, structural, cultural, or biological components of a system are numerous, heterogeneous, and linked, an increased degree of complexity is expected. In other words, complexity refers to the variations of the working components of a system that mistakenly may seem unrelated and random but actually formed under the same genetic coding [47] or social construct.
2.2 The role of acoustic indices in understanding ecological complexity
Acoustic indices are mathematical functions that capture specific characteristics of sound by focusing on the frequency domain similar to the structure and complexity of acoustic signals [45]. The intrinsically linked relationship of sound with ecological processes as described through the discipline of ecoacoustics [48], along with the fact that acoustic environments are fundamental parts of ecosystems [49], gave birth to a new set of indices that quantify acoustic properties similar to complexity [35]. They highlight aspects of the ecology of an area following several assumptions on the frequential attributes of animal vocalizations.
Acoustic indices are designed to estimate the complexity of animal sounds and acoustic environments [35] in various contexts [45]. Amongst the most frequently used acoustic indices are the ACI, the bioacoustics index (BIO), and the Normalized Difference Soundscape Index (NDSI). ACI assumes that biotic sounds exhibit variability in intensities, while human-generated noise similar to traffic noise maintains constant intensity values. The purpose of ACI is to measure acoustic complexity by assigning greater importance to sounds that demonstrate amplitude modulation. Consequently, it reduces the significance of sounds with relatively constant amplitudes, as these may originate from anthropogenic noise sources [43]. Nevertheless, biological sounds are not the sole contributors of acoustic complexity. Several anthropogenic sounds are not constant and are produced sporadically in an acoustic environment, thus contributing toward the increase of sound complexity. The BIO was introduced to assess the relative abundance of avian species. BIO computes the dB mean spectrum and calculates the area under the curve within the frequency limits, originally set between 2,000 and 8,000 Hz [50]. NDSI aims to estimate the level of anthropogenic disturbance in an acoustic environment. It achieves this by computing the ratio of human-generated (anthropophony) to biological (biophony) acoustic components. A value of 1 indicates that a sound contains no anthropophony. In terms of frequency, anthropophony was initially defined as the 1−2 kHz frequency bin, while biophony referred to the 2–8 kHz frequency bins [51].
Noise described through the environmental noise indicators can hinder the effectiveness of the acoustic indicators due to masking. Low levels of acoustic complexity in an ecosystem could be the result of two reasons: (a) the lack of sporadic sound sources of irregular amplitude and (b) the presence of a constant and monotonous sound source of increased amplitude [43,45]. Acoustic indices work best when applied at ecologically meaningful times similar to bird dawn and dusk choruses and ecologically meaningful spaces similar to natural areas [52] with minimum to zero human-related activities.
Several anthropogenic sounds occupy the frequency range attributed to biological sounds [53] and under circumstances elevate the acoustic complexity levels causing false results. Likewise, several biological and geophysical sounds caused by the wind, rain, and insect noise could reduce the acoustic complexity levels also causing false results [54,55]. It is therefore understood that heavily urbanized environments that contain loud, constant, and monotonous sound sources result in acoustic environments of low complexity.
3 Materials and methods
3.1 Case study area
Thessaloniki is a coastal Mediterranean metropolis located in the northern part of Greece. It is the second largest city in Greece and the municipality has a population of 324,766 residents [56]. Thessaloniki is a walkable city which is a key aspect of sustainable urban development [57] and can be upgraded to the standards of the “15-min” cities [58]. There are numerous public spaces and urban green areas, but the most important asset of the city is its waterfront, which is a highly visited public space with restorative, cultural, and environmental properties.
The new waterfront of Thessaloniki is a linear site, with a restricted width and a length of 3,5 km. The singular character of the waterfront is like a ribbon that unfolds on the emblematic border between dry land and the sea [59]. Furthermore, a catalogued soundscape intervention named “the garden of sound” [60] was implemented in 2014 [61].
For this research, we focused on a smaller 1 km section of the waterfront called “Nea Paralia – New beach.” Along the stretch of the area, several structural features and urban furniture are present, including a bicycle path, 70 park benches, 300 pine trees, a wooden walkway that is parallel to the sealine, an observatory platform, a cafeteria, an artificial waterfall and other water features, a basketball court, and several other sports facilities. Both the city of Thessaloniki and the waterfront area present a diverse number of birds and nesting opportunities. Amongst the most commonly seen and heard bird species in the waterfront during the measurement period (April) are [62] grey herons (Ardea cinerea), yellow-legged gulls (Larus michahellis), doves (Columpa liva), Eurasian colared doves (Streptopelia decaocto), common blackbirds (Turdus merula), warblers (Sylvia melanocephala), great tits (Parus major), sparrows (Paser domesticus), goldfinches (Carduelis carduelis), magpies (Pica pica), hooded crows (Corvus corone cornix), and even medium-sized parrots named rose-ringed parakeets (Psittacula krameri).
This research was conducted during the second wave of the COVID-19 pandemic, when citizens were primarily allowed to travel for health and work-related reasons, as well as for exercise. Additionally, a curfew was imposed, prohibiting all citizen movement from 21:00 to 05:00 on weekdays and from 18:00 to 05:00 on weekends.
3.2 Data collection protocol
To test the effects of road traffic noise on the waterfront under consideration, measurements were conducted during the implemented lockdowns of April 2021. All sound level measurements, traffic counts, and sound recordings were conducted for a 5 working day period during the morning hours (10.00–11.00 am). The data collection procedure was then identically repeated during April 2022, to compare the results. The area is affected by a major road of heavy traffic that is parallel to the waterfront under study. As indicated in Figure 1, the major road receives vehicles from two vertical local roads.
The case study area showcasing the measurement points the traffic count points and several structural characteristics of the waterfront.
The sound level measurements and sound recordings were conducted simultaneously at 7 points within a distance of approximately 150 m from each other, at 1.5 m above ground level. The 01 dB Fusion class 1 smart noise monitor along with the GRAS 40AE free field microphone were used to collect noise data, with 51.2 kHz sampling frequency and a 1 s logging interval. The device was calibrated before data collection using a calibrator, as required for all Class 1 measuring instruments and in accordance with the specifications of EN61326-1:1997 + A1:1998. The software dBTrait v. 6.3.0 (ACOEM, Limonest, France) was used to process the environmental noise data collected.
Furthermore, stereo sound recordings were conducted simultaneously with the sound level measurements, using a TASCAM DR-05x digital sound recorder. The recorder was also placed at a height of 1.5 m above ground level and programmed to record at a 44.1 kHz sampling rate using the built-in omni-directional microphones to collect 24-bit uncompressed WAVE audio files.
The above sound level measurement and sound recording protocol was applied for 5 consecutive days during the lockdown period (April 2021) and for 5 consecutive days during the post-lockdown period (April 2022). The resulting noise and acoustic indicators were later averaged and statistically analyzed.
For this research, the A-weighted levels of L eq, L min, L max noise indicators, and the percentiles L 90, L 50, and L 10 [63] were collected. The L eq, also known as Equivalent Continuous Sound Level, is a metric expressed in dB(A) that is used to represent the average sound level over a specified time period [64]. The seven measurement points also served as receiver points where the sound level (L r) was calculated via the CadnaA noise prediction software [65]. L r regards the sound pressure level (SPL) calculated at a receiver level and obtained from the sound power level of the source [66], which in this case is the vehicle fleet.
Furthermore, using the same protocol, the alpha (α) acoustic indicators, ACI, BIO, and NDSI and the beta (β) acoustic indicator of spectral dissimilarity index (D f) were calculated. The R Statistics software [67] and, in particular, packages, seewave [68], and soundecology [69] were used.
3.3 Noise modeling and noise mapping
The detailed cartographic representation of the waterfront and the urban area under study was crucial in order for the noise modeling and noise mapping results to be accurate. Structural information regarding height and exact location of buildings and vegetation were collected. The above structural information was imported in the QGiS software (v. 3.22.1 Białowieza) [70]. The guidelines provided by the CNOSSOS-EU road traffic noise model [71,72] were implemented to assess the noise conditions of the area through traffic flow composition. The manual method of traffic flow documentation that was conducted is presented in the following chapter. The CNOSSOS-EU noise model using the CadnaA noise prediction and noise mapping software [73] considers the digitized roads as sources of noise and the digitized urban structures (buildings) as obstacles. The following information was collected:
Traffic light location and time of operation;
Road type classification (local road, motorway, etc.);
Road surface type;
Road width; and
Vehicle speed: in this case, 50 km/h for passenger vehicles and motorcycles and 40 km/h for heavy vehicles.
Furthermore, the reference conditions used for the road traffic noise model included constant vehicle speed, flat and dry road surfaces, and no studded tires.
To provide the noise model with accurate input data, short-term vehicle classification counts were conducted. More specifically, the vehicles traveling the major road and the two local roads (Figure 1) were counted and classified in (a) light vehicles, (b) medium-heavy vehicles, (c) heavy trucks, and (d) two wheelers, as indicated in the CNOSSOS-EU road noise model [71]. For simplification reasons, all powered two-wheeler type of vehicles was listed according to the subclass of mopeds (category 4a) as specified in the “Guidelines for the competent use of CNOSSOS‐EU.” The acceleration and deceleration of all vehicle categories were considered before and after crossings with traffic lights in the area under study. The effect regards the change in vehicle speed when approaching or moving away from a crossing. By modifying similar manual methods [74,75], a counting protocol was specifically designed for this area (for more details, refer to supplementary material). The traffic volume data were collected via 1-h manual counts [76] using 1-min intervals, during the morning rush hours (09.00–10.00 am) of April 2021 and then repeated for the post-lockdown period of April 2022 [77].
3.4 Statistical analysis
The research objectives of this study were investigated using the IBM SPSS Statistics v. 25 and the R programming language [67]. The relationship of the noise and acoustic indicators was examined through statistical comparisons and correlations, to assess the effects of the reduced human mobility on the acoustic environment of Thessaloniki’s waterfront.
The statistical analysis was performed on the average values of all indicators for each measurement point across the 5-day period to obtain a single representative value for each point. The descriptive statistics provided information regarding the mean and standard deviation of the resulting values, along with the measure of shape and symmetry of distribution for the resulted noise and acoustic indicators. According to the Shapiro–Wilk test of normality, the distribution of the data collected was not significantly different from the normal distribution (sig. >0.05). Therefore, since the assumption of normality had been met, parametric tests were conducted.
A paired samples t-test was carried out to identify whether there were significant differences between the noise and the acoustic indicator levels accordingly for each period of measurement. Furthermore, to examine the relationship between noise and acoustic indicators, correlation tests were conducted. The results of the correlation analysis provided insights into the strength and direction of the association between noise levels and the acoustic indicators for each period of measurement.
Finally, a simple linear regression analysis was conducted to determine if environmental noise can be used to predict the levels of the acoustic indicators. The assumptions including the linearity of the relationship, homogeneity of variance, and independence of observations were considered. By performing correlation tests and simple linear regression analysis for the lockdown and post-lockdown periods, we gained valuable insights into the relationship between noise and the acoustic indicators during the lockdown period.
4 Results
The manual traffic counts conducted for the roads under consideration resulted in a fleet of vehicles for the studied period. The results for all vehicle categories, road category, and period were averaged and presented in Table 1. A total of 9,333 vehicles of all categories for the major road were counted for the post-lockdown period of 2022 and a total of 1,036 vehicles for the lockdown period of 2021. Powered two-wheeler type of vehicles presented high numbers in both periods. This is not an uncommon phenomenon in Mediterranean cities, as they are regularly chosen as a means of transport for small distances.
The vehicles counted for the lockdown period of 2021 and the post-lockdown period of 2022
| Type of road | Major road | Local road 1 | Local road 2 | |||
|---|---|---|---|---|---|---|
| Period | Lockdown | Post-lockdown | Lockdown | Post-lockdown | Lockdown | Post-lockdown |
| Light vehicles | 377 | 3,380 | 290 | 1,045 | 338 | 691 |
| Medium-heavy vehicles | 200 | 1,426 | 156 | 775 | 204 | 472 |
| Heavy-trucks | 60 | 108 | 45 | 123 | 65 | 55 |
| Powered two-wheelers | 399 | 4,419 | 384 | 996 | 441 | 628 |
The total number of all types of vehicles, the percentages of the available categories, and vehicle speed are the main input variables of the road noise traffic model and of the noise exposure maps created. In Figure 2, the noise exposure maps for the two periods are presented. The obvious change in noise emission is the result of reduced human activity due to mobility restrictions.
Noise maps created using traffic count data from the lockdown period of April 2021 and post-lockdown period of 2022.
Tables 2 and 3 present descriptive statistics that provide information on the mean and standard deviation of the resulting values, as well as measures of shape and symmetry of the distribution for both the noise and acoustic indicators.
Descriptive statistics for the noise and acoustic indicators during the lockdown period of 2021
| Descriptive statistics | ||||||
|---|---|---|---|---|---|---|
| Lockdown 2021 | ||||||
| Noise indicators | Min. | Max. | M | SD | Skewness | Kurtosis |
| L eq | 52.5 | 63.2 | 56.4 | 4.05 | 0.685 | −0.732 |
| L min | 45.3 | 54.0 | 49.2 | 3.41 | 0.333 | −1.778 |
| L max | 55.3 | 67.7 | 62.5 | 4.33 | −0.567 | −0.098 |
| L 90 | 45.8 | 57.2 | 51 | 4.06 | 0.426 | −0.963 |
| L 50 | 51.9 | 63.1 | 55.9 | 4.21 | 0.0949 | −0.308 |
| L 10 | 54.3 | 65.0 | 58.5 | 4.07 | 0.454 | −1.159 |
| L r | 49.5 | 58.0 | 54.9 | 2.84 | −0.873 | 0.084 |
| ACI | 3537.84 | 4640.36 | 4079.34 | 355.40 | 0.012 | 0.294 |
| NDSI | −0.2431 | 0.4807 | 0.2029 | 0.2939 | −1.065 | −0.880 |
| BIO | 5.33 | 6.78 | 5.9119 | 0.55433 | 0.667 | −1.237 |
Descriptive statistics for the noise and acoustic indicators during the post-lockdown period of 2022
| Descriptive statistics | |||||||
|---|---|---|---|---|---|---|---|
| Post-lockdown 2022 | |||||||
| Noise indicators | Min. | Max. | M | SD | Skewness | Kurtosis | Difference |
| L eq | 70.3 | 73.4 | 71.7 | 1.127 | 0.670 | −0.757 | 15.3 |
| L min | 50.5 | 61.3 | 55.9 | 3.377 | −0.006 | 0.790 | 6.4 |
| L max | 74.9 | 85.7 | 78 | 3.983 | 1.516 | 1.641 | 15.5 |
| L 90 | 51.8 | 65.6 | 58.4 | 4.623 | 0.324 | −0.299 | 7.4 |
| L 50 | 69.6 | 73.2 | 71.2 | 1.067 | 0.575 | 2.502 | 15.3 |
| L 10 | 73.4 | 76.1 | 74.4 | 1.064 | 1.032 | −0.783 | 15.9 |
| L r | 69.5 | 74.4 | 72.4 | 1.542 | −1.174 | 2.369 | 17.5 |
| ACI | 1714.81 | 3786.94 | 2483.75 | 707.75 | 1.134 | 0.945 | −1595.84 |
| NDSI | −0.4583 | −0.3002 | −0.3772 | 0.0495 | −0.087 | 0.282 | −0.5801 |
| BIO | 4.15 | 6.06 | 5.16 | 0.6507 | −0.075 | −0.413 | −0.7519 |
The mean difference between the post-lockdown and lockdown period is reported.
As indicated in Table 3, the measured mean differences of the A-weighted noise levels and the calculated noise exposure levels (L r) and the L eq values present the highest dissimilarities amongst periods.
As can be seen in Table 4 and Figure 4, the mean differences that derived amongst the noise indicators range from 6 to 17 dB(A). The extracted noise indicators showed several significant differences as indicated by the statistical test. More specifically, the expected differences regarded the L eq levels with t(6) = 10,351, p < 0.001, with an effect size of D = 3,912 and a mean difference of 15.24 dB(A). Furthermore, the calculated L r levels also present significant differences (t(6) = 22,681, p = 0.001) with a mean difference of 17 dB(A).
Paired samples t-test for the extracted indicators
| Paired samples test | ||||||
|---|---|---|---|---|---|---|
| Pairs | Mean | Std. deviation | t | df | Two-sided P | Cohen’s D |
| L eq post-lockdown L eq lockdown | 15.24 | 3.896 | 10,351 | 6 | <0.001 | 3.911 |
| L min post-lockdown L min lockdown | 6.71 | 4.72 | 3,764 | 6 | 0.009 | 1.421 |
| L max post-lockdown L max lockdown | 15.45 | 5.31 | 7,692 | 6 | <0.001 | 2.909 |
| L 90 post-lockdown L 90 lockdown | 7.35 | 5.27 | 3,693 | 6 | 0.010 | 1.394 |
| L 50 post-lockdown L 50 lockdown | 15.31 | 4.06 | 9,969 | 6 | <0.001 | 3.770 |
| L 10 post-lockdown L 10 lockdown | 15.81 | 3.89 | 10,742 | 6 | <0.001 | 4.064 |
| L r post-lockdown L r lockdown | 17.58 | 2.05 | 22.681 | 6 | 0.001 | 8.575 |
| ACI post-lockdown ACI lockdown | −1595.58 | 700.55 | −6.026 | 6 | 0.015 | 2.277 |
| NDSI post-lockdown NDSI lockdown | −0.5801 | 0.3076 | −4.989 | 0.002 | 1.885 | |
| BIO post-lockdown BIO lockdown | −0.7509 | 0.5224 | −3.803 | 0.009 | 1.437 | |
Statistical analysis revealed significant differences in all acoustic indicators when comparing different periods. Notably, there was a drop in acoustic indicator levels during the post-lockdown period. Additionally, the post-lockdown period exhibited several outliers and extreme values, which were particularly highlighted in the data (Figure 3).
Boxplots of the mean differences of the noise and acoustic indicators for the two periods of measurement.
Moreover, as it can be seen in Figure 4, the resulted NDSI values highlight the prevalence of antopophony in the waterfront’s acoustic environment during the post-lockdown period.
The resulted NDSI levels for both periods. The negative NDSI values indicate the prevalence of anthropogenic sounds in each of the seven measurement points.
A Pearson correlation analysis was conducted to assess the relationship between noise and acoustic indicator levels for the post-lockdown and lockdown periods. The results showed several positive correlations between noise indicators in both sampling periods. However, no correlations were found between the noise indicator levels and the acoustic indicator levels during the post-lockdown period of 2022. Similarly, no correlations were indicated between the NDSI and BIO and the noise indicators for the lockdown period of 2021. Notably, a negative correlation was observed between the measured L eq indicator levels and the ACI levels (r = −0.791, p = 0.034 < 0.05) during the lockdown period.
Since the L eq and ACI levels for the lockdown period of 2021 exhibited a linear relationship, and absence of outliers and extreme values, a simple regression analysis was conducted to assess the relationship between these variables.
The regression analysis revealed a significant regression equation (F(1, 5) = 8.353, p = 0.034 < 0.05), with an R 2 of 0.626. As can be seen in Figure 5, the fitted regression model was ACI = 7,993−69 × L eq
Scatter plot of ACI and L eq values during the lockdown period.
The model accounted for 62.6% of the variance in the dependent variable. The results indicated that the L eq values of the waterfront’s acoustic environment significantly predicted the ACI values (β = −69.3, p = 0.034 < 0.05). The negative coefficient suggests that as the independent variable (L eq) increases, the dependent variable (ACI) tends to decrease.
5 Discussion
This research article explored the effects of a prolonged anthropause due to the pandemic lockdowns, on the acoustic environment of a waterfront. Sound level measurements and sound recordings were conducted during the lockdown of 2021 and then repeated during the post-lockdown period of 2022, to collect noise and complexity data. For noise, the L eq, L min, L max, L 10, L 50, L 90, and L r noise indicator levels were collected. Additionally, complexity was measured through the ACI, the BIO, and the NDSI.
During the lockdowns, various types of urban spaces showcased an improved acoustic quality, as evidenced by the noticeable reduction in sound levels [78]. Similarly, implementing noise control initiatives that include traffic noise abatement in urban environments can lead to decreased noise levels [79,80,81], analogous to the acoustic conditions observed during the lockdowns.
In the case of the studied waterfront, we observed not only reduced noise levels but also an increase in sound complexity levels, contributing significantly to its overall enhanced acoustic quality. Therefore, one of the benefits of successful noise control initiatives could be the enhanced levels of sound complexity in an area.
Based on the findings of this study, there was a noticeable 15 dB(A) increase in the measured equivalent continuous sound level (L eq) index following the lockdown period. Additionally, a corresponding 17 dB increase in the calculated SPL (L r) was observed. The L r results were solely based on the noise modeling conducted using the vehicle counting protocol. Importantly, the calculated L r (17 dB increase) only considers road traffic noise as the source, while the measured L eq (15 dBA increase) values encompass a broader range of sound sources and are highly influenced by the people using the waterfront. Furthermore, the background noise levels (L 90) displayed a relatively minor rise of 7.4 dB(A) after the lockdown. This finding highlights the more subtle yet significant changes in the waterfront’s acoustic environment.
Most of the similar studies have relied on permanent noise monitoring stations or noise and soundscape-related projects that continued recording during the lockdown periods. These studies compared data collected before the implemented lockdowns with data during the lockdowns [78,82], resulting in varying reductions in different area types, with a maximum difference of 10 dB. As the lockdown restrictions eased and urban mobility increased, there was a rise in environmental stressors such as noise and air pollution [83,84]. Recent research that studied the fluctuations of the noise levels before, during, and after the lockdowns, highlighted substantial reductions that reached 20 [23], 15 [85], and 10 dB(A) [86].
However, similarly with the findings of this research, noise levels began to rise again once the lockdowns were lifted. This is likely due to the increased traffic and industrial activity as economies reopened. Nevertheless, reduced noise levels during the restrictions were not always the case. It has been discussed that in terms of noise, the reduction in traffic volume was exchanged with increased vehicle speed, thus resulting in equally increased noise levels [87].
The rationale of the acoustic indicators states that biological sounds occupy a specific frequency range which in most cases is above 2,000 Hz. It is understood that the effectiveness of acoustic indicators similar to ACI and BIO is uncertain in urban environments, as it is not uncommon for anthropogenic sounds to overlap in this frequency range [88]. In this research, significant decreases in the ACI and BIO levels after the lockdown period were documented. This observation could be due to the fact that the waterfront studied is not a heavily urbanized area as permanent populations of numerus bird species are documented. Previous research that investigated the levels of NDSI and BIO in various urban environments before and during the lockdown resulted that the overall NDSI values were higher during the lockdown period [89]. Nevertheless, BIO levels were found to be lower during the lockdown which contradicts the findings of our research. As discussed by the authors of the specific research, a possible explanation for this result could be that before the lockdown anthropogenic sounds occupied the frequency range attributed to biophony [89]. Therefore, the removal of these anthropogenic sounds that mistakenly categorized as biological, contributed to the reduction of the BIO levels.
To investigate the relationship between noise and acoustic indicators, correlation tests were performed. The results indicated positive correlations amongst all noise indicators during both measurement periods. Interestingly, no significant correlations were found between the acoustic indicators, particularly in the post-lockdown periods. However, an interesting finding emerged during the lockdown period, showing a negative correlation between the L eq noise indicator and the ACI levels. This inverse relationship could be attributed to the masking effect of noise on various sources of sound, which may contribute to increased complexity in the overall acoustic environment. However, this result contradicts similar research that resulted into a positive correlation between traffic noise and ACI levels [90]. The specific result was justified as an effort of birds trying to overcome such an acoustic intrusion by emphasizing their signals and therefore, increasing acoustic complexity.
Additionally, the regression analysis emphasized how human-generated noise impacts the acoustic complexity levels in urban areas. The inverse association of noise and complexity could be translated as an additional effect of noise on the quality of the acoustic environment. Previous research has described the acoustic environment of a city as an acoustic palimpsest [91], composed of several layers of sound. During the lockdown, the layer of road traffic noise was removed, which likely made other sound sources contributing to a more complex acoustic environment more audible. Therefore, the lockdown did not necessarily result in an increase of the actual levels of ecological diversity and complexity, since in this case, the overall sound levels of road traffic could simply mask the biophony of the area.
The post-lockdown acoustic environment likely led to non-linear relationships amongst the indicators due to increased chaos and variability in noise and complexity levels. This can be attributed to a multitude of factors, including changes in human activity, transportation patterns, and other environmental influences. These intricate interactions underscore the importance of gaining a comprehensive understanding of the acoustic environment, considering its dynamic and non-linear nature in real-world settings.
5.1 Limitations and future research
The lack of a permanent noise monitoring and sound recording network in Thessaloniki’s waterfront was the reason for compromising in a short-time sampling period during the lockdown of April 2021. A before, during, and after the lockdown measurement scheme, focusing on the levels of acoustic indicators could result in even more significant results.
As mentioned above, the examined section of the city’s waterfront includes a waterwall that runs parallel to the primary road that was considered in the evaluation (Figure 6). One of the significant advantages of waterscape sounds is the ability to mask road traffic noise and contribute to well-being [92].
The section of the artificial waterfall.
Future studies will involve an investigation of the waterfall’s impact on the levels of acoustic complexity, while assessing the factors that contribute toward complexity in an urban environment. Additionally, elements of the urban environment that either promote or reduce sound complexity will be studied.
Furthermore, the effectiveness of acoustic indicators in urban soundscape will be evaluated. The effort of linking various acoustic indicators similar to ACI, with the eventfulness and pleasantness of an acoustic environment will provide an additional tool towards sustainable urban soundscape planning and design.
Finally, we urge the soundscape researchers that have conducted similar research during the pandemic, to re-visit their data and analyze the interplay of noise with acoustic complexity.
6 Conclusions
Through this research, the effects of the reduced human mobility on the acoustic environment of a waterfront in a Mediterranean coastal city were documented. By conducting this research during both the lockdown and the post-lockdown periods, we were able to gain insights into the effects of reduced human mobility on the acoustic environment and the performance of the acoustic indicators. This information contributes to our understanding of the relationships between human activities, biodiversity, and the acoustic characteristics of the environment.
This research provided additional evidence regarding the observed reduced noise levels that occurred during the lockdown periods. Furthermore, it highlighted differences in the acoustic complexity levels of the area. Sound complexity could stand as a sustainable urban planning and design goal in an effort to create more livable cities. Therefore, the development of a new indicator concerning sound complexity, tailored for urban environments, capable of integrating both biological and anthropogenic sounds in real-world conditions, interactive with environmental noise, predictable, and amenable to modeling, would represent a valuable tool for urban sustainability. The focus should be on creating an acoustic environment that is diverse, interesting, and complex, rather than simply attempting to eliminate noise in a broader sense. This approach is in line with the growing trend towards acoustic environments that are more natural, dynamic, and culturally relevant, as opposed to ones that are solely focused on minimizing noise.
Acknowledgments
We sincerely thank the anonymous reviewers for their valuable comments.
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Funding information: We acknowledge the support of this work by the project “Center of Sustainable and Circular Bioeconomy [Aegean_BIOECONOMY]” (MIS 5045851) implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020), and co-financed by Greece and the European Union (European Regional Development Fund).
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Conflict of interest: Authors state no conflict of interest.
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Articles in the same Issue
- Regular Articles
- Statistical modeling of traffic noise at intersections in a mid-sized city, India
- Framework for urban sound assessment at the city scale based on citizen action, with the smartphone application NoiseCapture as a lever for participation
- Case study on the audibility of siren-driven alert systems
- Noise pollution and associated health impacts at Ganeshpeth Bus Terminus in Nagpur, India
- Reliability of smart noise pollution map
- Three-dimensional visualisation of traffic noise based on the Henk de-Klujijver model
- Assessment of noise pollution and associated subjective health complaints in Jharia Coalfield, India: A structural equation model analysis
- Exploring relationships among soundscape perception, spatiotemporal sound characteristics, and personal traits through social media
- Dynamic system employed for predicting noise emission at new constructed mineral ore processing plant
- Special Issue: Living with the Pandemic – Reflections on the Urban Sound Consequences of 2 Years of the COVID-19 Pandemic
- Montreal soundscapes during the COVID-19 pandemic: A spatial analysis of noise complaints and residents’ surveys
- Sound complexity as a strategy for livable and sustainable cities: The case of an urban waterfront
- Influence of soundscape on quality of work from home during the second phase of the pandemic in Brazil
- Special Issue: Latest Advances in Soundscape - Part I
- Prediction of the acoustic comfort of a dwelling based on automatic sound event detection
Articles in the same Issue
- Regular Articles
- Statistical modeling of traffic noise at intersections in a mid-sized city, India
- Framework for urban sound assessment at the city scale based on citizen action, with the smartphone application NoiseCapture as a lever for participation
- Case study on the audibility of siren-driven alert systems
- Noise pollution and associated health impacts at Ganeshpeth Bus Terminus in Nagpur, India
- Reliability of smart noise pollution map
- Three-dimensional visualisation of traffic noise based on the Henk de-Klujijver model
- Assessment of noise pollution and associated subjective health complaints in Jharia Coalfield, India: A structural equation model analysis
- Exploring relationships among soundscape perception, spatiotemporal sound characteristics, and personal traits through social media
- Dynamic system employed for predicting noise emission at new constructed mineral ore processing plant
- Special Issue: Living with the Pandemic – Reflections on the Urban Sound Consequences of 2 Years of the COVID-19 Pandemic
- Montreal soundscapes during the COVID-19 pandemic: A spatial analysis of noise complaints and residents’ surveys
- Sound complexity as a strategy for livable and sustainable cities: The case of an urban waterfront
- Influence of soundscape on quality of work from home during the second phase of the pandemic in Brazil
- Special Issue: Latest Advances in Soundscape - Part I
- Prediction of the acoustic comfort of a dwelling based on automatic sound event detection
