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Baseline assessment of underwater noise in the Ria Formosa
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Baseline
Baseline assessment of underwater noise in the Ria Formosa C. Soaresa,*, A. Pachecob, F. Zabela, E. González-Goberñab, C. Sequeirab a b
MarSensing Lda., Centro Empresarial Pavilhão B1, Campus de Gambelas, 8005-139 Faro, Portugal CIMA, University of Algarve Ed7, Campus de Gambelas, Faro 8005-139, Portugal
A R T I C LE I N FO
A B S T R A C T
Keywords: Underwater noise Anthropogenic noise Noise monitoring Soundscape Ship noise
The Ria Formosa is a sheltered large coastal lagoon located on the Atlantic South Coast of Portugal, that has been classified as a natural park since 1987. The lagoon hosts a diverse and abundant fish community and other species of commercial importance. Several economical activities are supported by shipping, and as such, vessel traffic within the Ria Formosa lagoon is very intense at some locations during particular seasons of the year, creating high levels of underwater noise. Recently, strong efforts are being made to turn the main inlet of the lagoon, the Faro-Olhão Inlet, a testing site for small scale tidal stream turbines, which will bring an additional source of underwater noise. Underwater noise can be one of a number of factors causing habitat degradation, as it can perturb fish behavior and cause physiological damage. Therefore, in order to comply with underwater noise pollution regulations, tidal energy technology developers are very interested in minimising the introduction of acoustic energy in the environment during the operation of their devices. Under the scope of project SCORE, which involved the deployment and operation of a floating tidal energy converter, this paper presents and discusses the first baseline noise monitoring performed at Ria Formosa. The acoustic data were collected in two occasions over several days, one in the winter and the other in the summer, in 2017. The obtained analysis results highlight the potential impact of the intense boat traffic in Ria Formosa, and the wide range of sound levels introduced in that ecosystem, and the high diurnal and seasonal variability.
1. Introduction
2012), and changes in behavior (Nowacek et al., 2007) including evasive tactics (Williams et al., 2002; Christiansen et al., 2010); and temporary or permanent damage in auditory systems of marine mammals and fish due to short or long-term exposure to anthropogenic noise. While the awareness on the noise effects was initially focused on marine mammals, as a consequence of a series of cetacean strandings in the 1990s, there has been an increasing concern regarding the effects of anthropogenic sounds on fishes (Popper et al., 2003) and marine invertebrates (André et al., 2011), since the inner ear hearing receptor of fishes are similar to those of marine mammals. In a study near Sicily, Sarà et al., observed the disruption of schools of bluefin tuna each time a passenger ferry approached (Sarà et al., 2007). While most studies on anthropogenic underwater noise focused in coastal and deep water areas, this study characterises baseline noise levels at an inshore location in Ria Formosa. Ria Formosa is a sheltered mesotidal coastal lagoon, located on the Atlantic south coast of Portugal. Shallow inshore areas such as coastal lagoons are highly productive, capable of sustaining great diversity and densities of organisms. The Ria Formosa lagoon supports a diverse and abundant fish community, with significant populations of juveniles of many commercially important fish species. It has been recognised as an important
During the last three decades, scientific and environmental organisations have been increasingly concerned with the problem of underwater noise as a form of pollution. Different sources contribute to the total noise budget in different marine areas. Shipping is considered the most significant contributor at the global scale (Hatch et al., 2008; McKenna et al., 2012). In some ocean locations, seismic exploration conducted by means of air guns can render extremely high noise levels for a variable extension of time. At a regional or local scale, local vessel traffic supporting fisheries and a number of other activities may be the most significant contributor to anthropogenic noise levels. More recently, with the advent of the offshore renewable energy, an additional type of noise source is potentially becoming increasingly significant, where usually distinct phases are considered — construction or deployment; operation; and decommissioning. Most of these noise sources are chronic, whose cumulative contributions represent anthropogenic pressures that may lead to habitat degradation. There are different known effects on marine animals, including masking of biologically relevant sounds (Erbe, 2002; Jensen et al., 2009), physiological stress (Wright et al., 2011; Rolland et al., ∗
Corresponding author E-mail address: [email protected] (C. Soares).
https://doi.org/10.1016/j.marpolbul.2019.110731 Received 16 June 2019; Received in revised form 6 November 2019; Accepted 11 November 2019 0025-326X/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: C. Soares, et al., Marine Pollution Bulletin, https://doi.org/10.1016/j.marpolbul.2019.110731
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and Tavira) and one natural inlet (Armona). The large embayment located behind the barrier system is occupied by salt marshes, sand flats and a complex network of natural and partially dredged channels. Thus, it encloses natural and artificial tidal inlets, exposed sandy beaches, salt marshes, sand flats and a complex network of tidal channels. It is a protected area since 1987, with the status of Natural Park, with the aim of preserving the lagoon system, protect the fauna and flora, migratory species and habitats, and promote the orderly use of the land and the natural resources, contributing to the economic, social and cultural development. Because it is a highly productive ecosystem, of great ecological diversity, it has been recognised as very important at both European and International level by its acceptance as part of the Natura 2000 European network for nature conservation, as RAMSAR wetland and included in the Special Bird Protection Area (hosting on a regular basis more than 20,000 birds during the wintering period). The tides in the area are semi-diurnal with typical average astronomical ranges of 2.8 m for spring tides and 1.3 m for neap tides. A maximum tidal range of 3.5 m can be reached during equinoctial tides, and over 3.8 m with surge setup. Wave climate in the area is moderate (an offshore annual mean significant wave height of Hs ≈ 1 m and peak interval, Tp of 8.2 s, with storms characterised by Hs > 3 m). Approximately 71% of waves approach from W-SW, with about 23% coming from E-SE. The lagoon is generally well mixed vertically, with no evidence of persistent haline or thermal stratification; owing to the reduced freshwater inputs and elevated tidal exchanges, the lagoon is basically euryhaline with salinity values usually close to those observed in adjacent coastal waters (Newton and Mudge, 2003). The wind is on average moderate (3 ms−1) and predominantly from the west (Williams and Magar, 2015). Variance analysis of both tidal and non-tidal signals has shown that the meteorological and long-term water-level variability explains less than 1% of the total recorded variance (Salles et al., 2005). The objective of the noise monitoring procedure was to establish a baseline measurement at the site. The acoustic data were collected at the main inlet (Faro-Olhão inlet) of Ria Formosa coastal lagoon with an autonomous hydrophone, the digitalHyd SR-1, over two periods, respectively in the winter and summer seasons. Fig. 2 (a) shows the study area with deployment positions. For the first deployment, marked with ×, acoustic data collected between 20th January and 1st February 2017 is taken into account herein, and for the second deployment, marked with ◊ acoustic data collected between the 1st and 22nd August was considered. In the first occasion, the acoustic recorder was deployed at latitude N36.9749 and longitude W7.8753, at water depth of approximately 11 m (considering mean sea water level). In the second occasion, the acoustic recorder was deployed at latitude N36.9741 and longitude W7.8718, at water depth approximately 13.4 m. Fig. 2 (b) shows the hydrophone mooring, made of a robust tripod structure deployed on the seabottom. The SR-1 autonomous hydrophone is a compact acoustic recorder equipped with a SensorTech SQ26 transducer ( sensitivity is −194 dB re 1 V/1 μ Pa with a variation within ± 1dB in the 1 Hz to 28 kHz interval), driving a 32 dB pre-amplifier, and a programmable gain amplifier (PGA) with selectable gains from 1×,2×,…,64 × (Soares et al., 2011). For the actual acquisition, the PGA was set to a gain of 16 ×, rendering an overall nominal sensitivity of −143 dB re 1 V/1 μ Pa; the sampling rate was set to 52,734 sps and amplitude resolution was set to 24 bits. The acquisition was scheduled to start every 10 min for 90 s, i.e. a duty-cycle of 15%. For each acquisition interval a WAV file was generated. The actual duty-cycle was sufficient to record boats passing within the receiver's position neighborhood. The total effective acquisition time was approximately 50 h for the deployment in the winter season and 76 h for the deployment the summer season. Current velocity was sampled with an ADCP during the acoustic data acquisition carried out in January. Fig. 3 shows the absolute value of current velocity. It is seen that the velocity peaks attain about 0.4 m/ s during date 20/01, from which the peak speed progressively increases
natural wetland with great economic value, classified as a natural park since 1987 and accepted as a Ramsar and Natura 2000 site. The lagoon supports many human activities, including tourism, aquaculture, shipping, fishing, harvesting of bait, salt production and sediment extraction (Ribeiro et al., 2006, 2008). Most of these activities are supported by the local vessel traffic, consisting of fisher boats, and passenger and recreational boats, and other small boats with inboard or outboard engines, and sailing boats underway with engines. The local vessel traffic is very intense in some areas of Ria Formosa over some seasons of the year, rising concerns on the effects caused by the introduction of noise on the local fauna. Most acoustic energy radiated by small boats with outboard or inboard engines has its spectral contents in the frequency range 0.1 to 5 kHz and source level 150 to 180 dB re 1 μ Pa (Hildebrand, 2009). These sounds are within the hearing range of many fish species, which often have best hearing sensitivity at frequencies below 1 kHz (Popper et al., 2003). Another relevant activity that has the potential to contribute for further increments of the noise level in Ria Formosa is tidal energy exploration. The Faro-Olhão Inlet, which is responsible for exchanging the largest volumes of water between the ocean and the lagoon, has a significant tidal stream energy resource (Pacheco et al., 2014; González et al., 2018). Under the SCORE project1, a 1:10 scale floatable tidal energy converter (TEC) prototype (Evopod E1 from Oceanflow Energy Ltd.) was deployed and operated for 5 months constituting the first time that a device of this characteristics was tested in Portuguese waters. The project outcomes proved the potential of Faro-Olhhão Inlet to become into a testing site for small TECs technologies (Pacheco et al., 2018). Project MARINERG-i is developing a scientific and business plan for an integrated European Research Infrastructure network in offshore marine renewable energy, which provides technology developers a network of testing sites (Marinerg-I Project, 2019). In the European Union (EU), most of the sites to test TECs are in the United Kingdom waters. With Brexit, the access to these facilities through EU funds is uncertain, which may constitute a serious drawback for the evolution of the tidal energy sector. Moreover, tidal stream energy is very site specific, this means that suitable regions for testing are not as common as for wave energy and offshore wind. Therefore, identifying and fully characterising new sites in EU waters, such as Faro-Olhão Inlet, is of vital importance to ensure the competitiveness of EU's ocean energy sector. However, in order to turn it into a recognised site and promote it to be included as part of the MARINERG-i research infrastructure network, a full and detailed characterisation of the site is needed. Establishing current baseline levels contribute to characterise the acoustic levels of the region and will allow tidal energy developers to assess the contribution of their technologies to noise pollution, providing valuable information to optimise their designs to mitigate this type of undesirable impact. The baseline study presented in this paper was conducted in the scope of the SCORE project (Pacheco et al., 2018). To our best knowledge, this is the first underwater noise monitoring ever conducted in the Faro-Olhão Inlet of Ria Formosa coastal lagoon. The paper introduces the main characteristics of the study region; describes the methodology approach, including aspects of the acoustic acquisition setup and data analysis; then presents the experimental results; and finally presents the main conclusions. The noise monitoring was conducted at Faro-Olhão Inlet, the main inlet of Ria Formosa system (hereafter RF), a coastal lagoon located in the South of Portugal (Fig. 1). The RF is one special location along European coastlines since is, with Wadden Sea, the two only multiple inlet systems of European coasts. It has about 18,400 ha, and 60 km of extension, presently comprising six islands and one peninsula separated by seven tidal inlets: four artificially opened or relocated (Ancão, Fuseta, Lacém and Cacela), two artificially stabilised inlets (Faro-Olhão
1 SCORE stands for Sustainability of using Ria Formosa Currents On Renewable Energy production.
2
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Fig. 1. Deployment site adjacent to Faro-Olhão Inlet (A), main inlet of Ria Formosa lagoon system (Algarve, Portugal), for future test site of tidal energy converters (B). Fig. 2. Acoustic recordings experimental setup at the Ria Formosa inlet. (a) work box with autonomous recorder deployment positions: January 2017 (×); August 2017 (◊). (b) Autonomous recorder mounted on a tripod.
Fig. 3. Current velocity as function of time during the acoustic acquisition period from 20th of January to 1st of February.
intervals of the day. The data indicates that wind was more intense during the day than during the night, over the relevant observation interval, attaining the maximum average speed during times 09:00 to 12:00. During the acoustic data acquisition carried out in August, no wind speed was sampled.
to 1.1 m/s at the end of the interval. During the acoustic data acquisition carried out in August no current velocity was sampled. Wind speed data was made available for the interval from 20th January to 1st February, whereas wind speed was sampled every 30 minutes at the Faro airport. Table 1 shows average wind speed over 3
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corresponding to the data acquired in January 2017. The spectrograms were obtained by averaging periodograms of 4096 samples segments to 90 s resolution via the Welch method, i.e. the total data portion acquired every 10 min. Each spectrogram is completed with the broadband level in dB re 1 μPa2 over time, integrating the acoustic power from 35 Hz to 22,449 Hz, merging the frequency coverage of all 1/3 octave bands considered in the analysis reported below. Recall that the site of deployment was in the main inlet close to a traffic route leaving or entering the Ria Formosa, and therefore idle and busy intervals of traffic were expected a priori to occur. In January there are effectively idle periods, with no boats passing during the nights of 22/01, 29/01 (Saturday to Sunday) and 23/01 (Sunday to Monday). During several other nights few discrete acoustic events related to boat traffic are noticed, where broadband levels as low as 87 dB re 1 μPa are observed. When boats cruise the area, broadband levels reach approximately 110 dB re 1μPa and peak above 120 dB re 1μPa few times. During the second week some periods with a broadband noise becomes visible, during intervals where current speed exceeds 0.8 m/s. Current speed may cause flow-noise or pseudosound, as a result of both advected ambient turbulence and interactions with the transducer in the flow, which will increase the acoustic pressure received at the transducer. This is seen as an interference rather than noise that was actually radiated by a source away from the transducer (Bassett et al., 2014; Strasberg, 1979). In the absence of discrete events during the night from 21st to 22nd of January, it is possible to observe a slow variability in the level of background noise. It is apparent that about midnight the level of background noise is maximized and after it progressively reduces until the first discrete events occur at time 05:15. This observation is in agreement with the peak in current velocity of 0.35 m/s attained just after midnight and the minimum speed achieved at time 04:45. Also over the night from 22nd to 23rd January a similar phenomena is observed where a maximum of 0.3 m/s in current velocity occurs before midnight and another peak of 0.35 m/s occurs after midnight, and a minimum velocity at about midnight. However, this relation was not always found. Further inspection on the contribution of current velocity on the measured noise level indicated that a significant contribution is seen when it exceeds 0.8 m/s. During the interval from 20th to 25th January current speed peaked between 0.25 and 0.7 m/s, and from 26th January to 1st February current speed peaked between at 0.2 and at 1.1 m/s.
Table 1 Average wind speed from 20th January to 1st February over intervals of 4 h. Time (hh:mm)
Wind speed (m/s)
00:00 04:00 08:00 12:00 16:00 20:00
3.9 4.1 4.9 4.6 3.6 3.6
to to to to to to
04:00 08:00 12:00 16:00 20:00 24:00
The acoustic data were processed in MATLAB using custom-written scripts. First a spectrogram was computed for each WAV file, computed by means of the Discrete Fourier Transform with a Hanning window of 4096 samples (equivalent to approximately 0.078 s), which yields 1185 instant frequency observations for each file. Each spectrogram was saved in its respective file for subsequent analysis. The subsequent analysis consisted of a statistical analysis both on broadband and 1/3 octave bands acoustic pressure. The objective was to characterise the soundscape based on indicators such as mean levels and percentiles as to obtain level exceedance in proportion of time, and spectral probability densities (SPD) (Merchant et al., 2013). Historically, the root of mean squared (rms) level is the most prevalent metric. However, it is strongly influenced by the highest instant sound levels and should be used carefully with transient sound sources (i.e. such as boats passing occasionally at the measurement position) (Merchant et al., 2012). The 1/3 octave bands use standard central frequencies, where the first band is centred at 39 Hz and a 9 Hz bandwidth; and the last band has 20 kHz central frequency and a 4631 Hz bandwidth. The width of each band is directly proportional to the centre frequency. The spectral slope of the fractional octave band levels differs from the power spectral density (PSD), as the bands are scaled logarithmically with frequency, meaning that the bands widen with increasing frequency. Therefore, higher frequency bands integrate energy over larger frequency ranges. The analysis of a time series can provide a broad idea on discrete events and temporal patterns. The spectrogram is widely used in time series analysis, as it allows for the observation of variations of the sound level with time over the frequency band. Visual inspection allows for the detection or even identification of discrete sound sources such as boats passing near or vocalisations of animals, since each type of sound source may show distinct spectral contents. Fig. 4 shows spectrograms
Fig. 4. Time series analysis of acoustic data acquired with an autonomous hydrophone in January 2017: 20th to 25th January (top panel); and 26th January to 1st February (bottom panel). The analysis has been performed using segments of 4096 samples (≈0.078 s) averaged to 90 s using the Welch method. 4
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Fig. 5. Time series analysis of acoustic data acquired with an autonomous hydrophone in August 2017: 2nd to 8th August (top panel); 9th to 14th August (middle panel); and 15th August to 21st August (bottom panel). The analysis has been performed using segments of 4096 samples (≈0.078 s) averaged to 90 s using the Welch method.
floor seen in January is taken as the reference background noise, then the perturbation caused by a boat passing in this channel is able to exceed the natural background noise by 40 dB or more, which can be considered dramatic. The visual analysis clearly suggests that there are two regimes in both data sets: a diurnal regime where the soundscape is contaminated to large extent by the local boat traffic; a night regime with reduced or inexisting boat traffic. In order to determine more exactly which are the busy and idle intervals in terms of noise level, a statistical analysis on 1/ 3 octave bands spectra has been carried. Fig. 6 shows percentile 50 obtained for 1/3 octave bands bins over 1-hour intervals for the complete acquisition periods in the winter (left panel) and in the summer (right panel). Time is local time. Instantaneous 1/3 octave bands spectra were computed from the FFTs (no averaging), and percentile 50 obtained for each 1-hour interval over all days comprised in each acquisition period. Percentile 50 indicates the level that is exceeded during 50% of the time. This analysis allows for discriminating the idle interval from the busy interval almost free of ambiguity, since there is a step of 10 to 15 dB in the intermediate region in the direction of the time axis (left panel), comprising almost the entire frequency band analysed. This somewhat ad-hoc criteria led to the decision that the busy interval is from times 07h00 to 17h00 of the day, and the rest of the time is the idle interval, in both data sets. The possibility of using Automatic Identification System (AIS) data was considered, but it was concluded that it would be of little use since most boats passing near are recreational or small fisher boats not equipped with that system, and hence these data would be not representative of the actual traffic in the working area. Two different regimes were identified on both data sets, an idle and a busy regime, as the result of diverse degrees of vessel traffic intensity at day and night, at weekdays and weekends, and in the winter and summer seasons. In opposition to the time series representation, which is more suited to describe events and trends in sound levels, now a statistical analysis is carried out to fully characterise the observed soundscape (Merchant et al., 2015), by taking into account the two
Wind measurements show that, in average, higher wind speed occurred during the day, ranging from 4.6 to 4.9 m/s (see Table 1). During the night average wind speeds from 3.6 to 3.9 m/s were observed. Similar values are observed when only diurnal and nocturnal periods are considered. The visual analysis of the spectrograms does not provide a discrimination of significant contributions of windspeed to the received noise level, as the noise floor tends to be lower during the day. In other words, in the January data set, the noise floor during the day is lower than at night, despite the normal increase of boat traffic at daytime. Several natural sounds of transient nature can be heard continuously, possibly caused by organisms contributing for increased natural background noise during the night. Fig. 5 shows spectrograms corresponding to the data acquired in August 2017. By comparing this time series with that of January, it becomes quite apparent that the two time series correspond to two different soundscapes, although with the same underlying phenomena — the local vessel traffic. In both cases, it is seen that there are busy intervals alternated with idle or relatively idle periods, centred at midnight. The soundscape in August is more consistently contaminated with noise radiated by boats, and peak values are more consistently reached and maintained, as sound level often approach or exceeds 120 dB re 1μPa level consistently, while in the soundscape in January the discrete noise events are less overlapped. In August, the spectral power has significantly increased over the entire frequency range. The minimum noise level is observed around times 05/08 (Saturday to Sunday), and 06/08 (Sunday to Monday), and around time 13/08, although with a few boat passages observed, hence not completely free of boat noise. This comparison also shows that the noise patterns in January and August are significantly different due to increased vessel traffic intensity in the summer months. While in January the broadband level is characterised by peaks that tend to be time discrete and discriminable in many occasions in August the events overlap and therefore the broadband level tends to remain sustained during the whole day with a smooth and progressive variation at dawn and evening. If the noise
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Fig. 6. Percentile 50 (median) for 1/3 octave noise levels for each hour of the day over the entire acoustic acquisition interval in each season: winter season in January 2017 (left); summer season in August 2017 (right).
be seen as the weighted average between the busy and idle regimes. Another feature to consider is that the curves show a relatively long tail after the 5% mark, which includes observations exceeding the 5% mark by more than 25 dB. This is presumably caused by boats that passing near the hydrophone, which become statistical outliers in the acoustic data set. The outcome of the comparative result between the busy and idle intervals comes from the fact that the noise events are discrete in time with relatively short transient times, and with levels outstanding significantly from ambient noise. The summer data shows a different behavior, concerning the comparison between the two periods. All curves are clearly separated with no overlapping. In this case, the sound level variability is higher in the idle interval than in the busy interval, as the 95% marks are about 7 dB apart, and the 5% marks are only 5 dB apart. This agrees with the fact that noise level is sustained during the busy interval, showing the progressive increase/reduction of local boat traffic at the beginning/ end of the day, respectively. Table 2 summarizes the statistical results on broadband SPL by means of percentiles and rms levels for the three analysed intervals. The percentiles reflect the information given in the form of level exceedance in proportion of time presented in Fig. 7. The results show that the actual site is subject to quite intense noise levels. It seems to be acceptable to take percentile 1 as a reference for the natural sound level, because no anthropogenic noise source was noticed during three nights in January, which exceeds more than 1% of the total observation time. It is assumed that during such intervals the natural ambient noise level was stationary, without statistically significant variability from natural sources (e.g. weather, tidal processes, soniferous fish and invertebrates). Hence, if percentile 1 is taken as the natural sound level, which would be about 84 dB in the winter, then the level increment over significant time intervals can exceed approximately 41 dB during 1% of the observation time in the summer season. It is also shown that while in the winter the idle to busy rms SPL ratio is about 4 dB, in the summer this ratio is about 9 to 10 dB. As a complementary analysis in the time domain, and to assess the diel variability of BB SPL, Fig. 8 shows boxplots for 1 hour intervals over the entire acquisition period, for the winter data set (left panel) and the summer data set (right panel). The black mark is the median,
daily intervals, over the two observation periods. In order to establish the baseline for the actual area, first broadband noise levels are obtained, and then a frequency analysis is provided, in terms of significant variability and distribution. Next, an analysis on broadband noise levels is provided. As stated above, broadband level refers to the sound level integrating all power contained in the frequency band from 35 Hz to 22,449 Hz, which is the range of frequencies covered from the lower frequency bound of the first 1/3 octave band to the upper frequency bound in the last 1/3 octave band considered herein. In this way, coherence between the broadband sound pressure level (SPL) and 1/3 octave level analyses is maintained. Fig. 7 shows Level Exceedance in Proportion of Time for the two complete data sets. To estimate the level exceedance for a given proportion of time, the instantaneous SPL observations obtained from data windowing within a given interval of the day were collected and ranked in ascending order as to extract the percentiles from zero to 100 with a resolution of 1. Then the percentile was simply reversed for conversion to percent — the proportion of time. For example, percentile 5 corresponds to the SPL exceeded during 95% of the time. The curve obtained by this processing would be similar to that obtained by computing the cumulative distribution of the samples, in particular, for a large number of realizations. This analysis was carried out for the two comparison intervals, from times 7h00 to 17h00 (blue curve), and from times 17h00 to 07h00 (black curve), and for the full acquisition time (gray curve). For comparison, markers at proportions 95% and 5% of the time have been included. The panel on the left of Fig. 7 corresponds to the winter data, and the panel on the right corresponds to the summer data. Considering the winter data first, it is seen that the variability over the diurnal period is higher then over nocturnal period, since the blue and black curves cross at 80% of time, which confirms that during the busy interval the noise floor is lower that during the idle interval. Towards the upper end, the results indicate that the diurnal period (busy interval) is louder than the nocturnal period. The level exceedance for 5% of the time is 112 dB re 1μPa for the diurnal period (blue), 107 dB re 1μPa for the nocturnal period (black). The gray curve comprises the complete data set, and therefore shows a result that can
Fig. 7. Broadband sound level exceedance in proportion of time: January 2017 (left); August 2017 (right). Busy interval is from 07h00 to 17h00 (blue); idle interval is from 17h00 to 07h00 (black); all data (gray). The circle markers indicate level exceedance for 95% and 5% of the time. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Table 2 Broadband sound pressure level: statistical analysis summary for the acoustic data collected in the two seasons, over the three intervals. All day comprises all data; diurnal comprised all data collected during day, from 07h00 to 17h00 local time; nocturnal comprised all data collected during night, from 17h00 to 07h00 local time. Percentiles pn and rms pressure values are given in dB re 1 μPa. Season
Day/night
p1
p5
p25
p50
p75
p95
p99
rms level
Winter
All day Diurnal Nocturnal All day Diurnal Nocturnal
84.4 83.8 84.9 90.0 94.1 89.5
86.5 86.0 86.9 92.1 98.6 91.3
89.7 90.3 89.6 96.5 105.4 94.5
92.6 95.9 91.6 103.7 110.0 98.0
98.0 102.4 95.0 110.7 114.1 104.8
109.1 112.0 105.0 118.0 120.0 114.8
117.1 120.8 112.8 123.3 125.1 121.0
94.7 97.0 93.1 104.0 109.7 100.2
Summer
the box edges are the first and third quartiles, and the blue circles indicate percentiles 1 and 99 (whiskers). This result describes the dependence of the noise level with time of the day, as a result of the boat traffic nearby. In both data sets, there is a high degree of skewness for times in the idle interval, where the summer data shows a slightly lower asymmetry for that period, due to longer interquartile ranges. During the busy interval, from times 07h00 to 17h00, the statistical behavior in both cases can be distinguished as follows: in the winter data, percentile 25 remains nearly constant at all time, and there is an evident increase in the interquartile range during the busy interval, but skewness remains. In the summer data, there is an onset of the box in the centre of the significant interval, rendering a nearly symmetric distribution, and the interquartile range is slightly reduced in comparison to the idle interval, except for time from 19h00 to 01h00. Concerning the whiskers, the behavior is distinct too: in the winter data set percentile 1 progressively reduces during the day, reaching the minimum at time 14h00, while percentile 99 increases significantly in comparison to the idle interval; in the summer data the whiskers show two distinct periods, i.e. percentile 1 shows an onset to 90 dB from time 20h00 to time 10h00, and a variation between 95 and 100 dB from time 11h00 to time 19h00, which indicates a change from discrete acoustic events to sustained or overlapping acoustic events. The winter data shows an outlier for percentile 99 at time 12h00, whose origin can be spot in the time series of Fig. 4, where an increased transient level is seen at about 12h00 at days 22nd, 23rd, and 25th of January. The actual results on the broadband noise levels shown here provide a thorough quantification of the features seen in the time series analysis above, both over different time intervals and the complete interval of each data set. The statistical analysis carried out for the broadband levels can be applied in the frequency domain for the assessment of the variability over significant spectral bands. In the actual case, this analysis provides an idea on the acoustic contamination introduced by the noise sources over the observed frequency band, by comparing the busy and idle regimes. Fig. 9 shows boxplots for the two data sets over 1/3 octave bands, with the winter data set on the left column, and the summer data set on right column. The frequencies are the centre frequencies of the 1/ 3 octave bands, whose width increases logarithmically with frequency. This should be accounted for, since the plotted amplitudes represent the power integration over the respective 1/3 octave band.
In general, the boxplots indicate that the levels in bands with centre frequencies up to 78 Hz, and about 5 kHz, received reduced influence from the boat traffic, since the increase from idle to busy interval, or from winter to summer is relatively reduced. Towards the lower end of the band, those bands show symmetric distributions, and towards the higher end the skewness progressively increases. Above the 78 Hz band, the increment in sound level is more noticeable when comparing idle to busy interval, and winter to summer season. The distribution symmetry increases from winter to summer data set, in particular, in the bands from 98 to 5 kHz, indicating that in this band the noise contamination during the summer is sustained. Within this band, when comparing the idle interval in winter to busy interval in summer, the increase in level is approximately 15 dB, in all bands. The boxplot analysis for the winter data set shows that percentile 1 and percentile 25 are lower in the busy interval than in idle interval for frequencies above 1575 Hz and 1984 Hz, respectively, which gives an insight on the minimum noise floor shown in Fig. 8 (left panel). The increased high-frequency content during the idle interval could be of biotic origin, from organisms such as shrimps and sea fleas with increased acoustic activity during the night. The bottom boxplots of Fig. 9 are for all data, and is therefore a combination of the other two obtained for idle and busy intervals. Fig. 10 shows the spectral probability density (SPD) (Merchant et al., 2013), whereby the empirical probability of sound levels as a function of frequency is presented, with superimposed percentile curves across the frequency band for percentiles 1, 5, 50, 95 and 99. The left and right panel correspond to the data sets collected in January 2017 and August 2017, respectively. Only complete data sets are shown for this indicator. This analysis can reveal multi-modality in the received sound level. In actual data sets, the observed noise level has a unimodal characteristic, although two very distinct regimes were identified throughout the analysis of these data sets. It can be seen that while for the winter data the distribution tends to be concentrated around the median, for the summer data the level distribution peak has shifted significantly towards higher levels, for frequencies between 100 and 1000 Hz, and for frequencies above 1000 Hz the level is more spread over the SPL interval. In the later case the sample realizations suffer increased spreading across the sound level interval. These empirical distributions complement the frequency boxplot as it clearly reveals how the noise levels are distributed over narrow
Fig. 8. Boxplot for broadband SPL over 1 hour intervals of the acoustic data acquired from 20th January to 1st February. The black mark is the median, box edges are the first and third quartiles, and the blue circles indicate percentiles 1 and 99. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 7
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Fig. 9. Boxplot of 1/3 octave bands for data sets collected in January (left) and August (right). The analysis is divided in nocturnal interval (top); diurnal interval (middle); and all time (bottom). The black mark is the median, box edges are the first and third quartiles, and the blue circles indicate percentiles 1 and 99 (whiskers). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Acoustic data has been acquired at an inlet of Ria Formosa coastal lagoon with an autonomous hydrophone moored by means of a tripod structure deployed on the seabottom at an average water depth of approximately 11 m, in two occasions in 2017, for 12 days in January, and for 21 days in August. The deployment site is characterised by high flow velocities due to tidal cycles and serves as passage of local boats. Due to fisher and touristic activities, the traffic intensity is very distinct from winter to summer season, and therefore distinct degrees of acoustic contamination was expected to be found. The acoustic data analysis was based on statistical processing on the broadband sound level, 1/3 octave bands, and FFT bins. In a first analysis step, by means of the estimation of the median of 1/3 octave band levels over 1 hour intervals, two intervals of acoustic intensity were established, as a result of distinct traffic intensity, which were called idle and busy intervals. Then data analysis was performed as to separate these periods. This separation proved to be important in maintaining the ability to estimate reference levels of the natural noise. The results indicate very distinct soundscapes from night to day, and from winter to summer seasons, as a results of the increased boat traffic in the summer. The mean broadband level in the summer is more than 9 dB higher than that of the winter. If percentile 1 in the winter is used to estimate the level of natural noise, than percentile 1 in the summer is increased by 5 dB (for all time), and during the day this level is exceeded by more than 40 dB during 1% of the time. The frequency analysis shows that the anthropogenic acoustic contamination is most severe in the frequency band 100 to 2000 Hz, since this was the interval with highest idle to busy interval ratios. And also for the reason that in the winter data the noise floor remained
frequency bands of equal width. For example, the noise in the frequency band above 2 kHz is more compact than below that frequency. The comparison of the two SPDs reveals that the increased vessel traffic in the summer season has most contributions within the 2 kHz band, but some increased spreading above that frequency is observed. The SPL interval goes well beyond the 1% and 99% percentile boundaries over most of the frequency band, indicating occurrence of outliers, in either case. Having outliers below percentile 1 is a good indication that the acoustic recorder self-noise did not limit the noise floor. Also, the degree of skewness over the frequency band can be observed. The skewness indicates on which side of the distribution is the tail. In the actual context, it provides an idea on whether the most frequent noise levels are on the lower side or on upper side of the distribution. Since these distributions are unimodal across frequency, herein, it is useful to interpret skewness in order to determine in which interval of frequencies the change between the two data sets was most significant. For example, in the frequency band 200 to 2000 Hz, the skewness was positive in January (left panel) but it was negative in August (right panel). This is a relevant change revealing that over that frequency range the noise level increment was most significant, while in the remaining frequency intervals the change in degree of skewness was relatively reduced. The pink curve shows an estimate of the average PSD estimated by integrating the SPL over the probability distribution at each frequency. Sound level in rms values must be analysed carefully since these values are provided in a logarithmic scale, i.e. outliers on the lower and upper end of the distribution and significantly contribute for the average level.
Fig. 10. Spectral Density Probability of noise level as a function of frequency with percentiles (black curves) and PSD (pink curve): January 2017 (left); August 2017 (right). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 8
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lower in the busy interval for frequencies above 1575 Hz, even in the presence of increased boat traffic. The actual baseline survey is the first reported noise monitoring in Ria Formosa, and highlights the potential impact of the intense boat traffic associated to fisheries and touristic activities. It also highlights the wide range of sound levels to which that ecosystem is exposed within the frequency band up to 23 kHz, and the signifcantly distinct noise levels observed over different seasons of the year, weekdays and weekends, and over night and day. Finally, with regard to tidal energy converters, noise surveys to assess the levels of acoustic energy introduced in the environment during the operation of the turbines should be conducted at night and, preferably, during the winter months. This will enable comparison with baseline conditions without the contribution of boat traffic, and to clearly identify the frequencies of noise radiated by an energy conversion device.
marine sanctuary. Environ. Manag. 42, 735–752. https://doi.org/10.1007/s00267008-9169-4. Hildebrand, J.A., 2009. Anthropogenic and natural sources of ambient noise in the ocean. Mar. Ecol. Prog. Ser. 395, 5–20. https://doi.org/10.3354/meps08353. Jensen, F.H., Bejder, L., Wahlberg, M., Soto, N.A., Johnson, M., Madsen, P.T., 2009. Vessel noise effects on delphinid communication. Mar. Ecol. Prog. Ser. 395, 161–175. https://doi.org/10.3354/meps08204. Marinerg-I Project, 2019. Planning an integrated European research infrastructure. http://www.marinerg-i.eu/. Online. McKenna, M.F., Ross, D., Wiggins, S.M., Hildebrand, J.A., 2012. Underwater radiated noise from modern commercial ships. J. Acoust. Soc. Am. 131, 92–103. https://doi. org/10.1121/1.3664100. Merchant, N.D., Barton, T.R., Thompson, P.M., Pirotta, E., Dakin, D.T., Dorocicz, J., 2013. Spectral probability density as a tool for ambient noise analysis. J. Acous. Soc. Am. 133, EL262–EL267. https://doi.org/10.1121/1.4794934. Merchant, N.D., Blondel, P., Dakin, D.T., Dorocicz, J., 2012. Averaging underwater noise levels for environmental assessment of shipping. J. Acous. Soc. Am. 132, EL343–EL349. https://doi.org/10.1121/1.4754429. Merchant, N.D., Fristrup, K.M., Johnson, M.P., Tyack, P.L., Witt, M.J., Blondel, P., Parks, S.E., 2015. Measuring acoustic habitats. Methods Ecol. Evol. 6, 257–265. https://doi. org/10.1111/2041-210X.12330. Newton, A., Mudge, S.M., 2003. Temperature and salinity regimes in a shallow, mesotidal lagoon, the Ria Formosa, Portugal. Estuar. Coast. Shelf Sci. 57 (1), 73–85. https://doi. org/10.1016/S0272-7714(02)00332-3. Nowacek, D.P., Thorne, L.H., Johnston, D.W., Tyack, P.L., 2007. Responses of cetaceans to anthropogenic noise. Mamm. Rev. 37, 81–115. https://doi.org/10.1111/j.13652907.2007.00104.x. Pacheco, A., Ferreira, O., Carballo, R., Iglesias, G., 2014. Evaluation of the production of tidal stream energy in an inlet channel by coupling field data and numerical modelling. Energy 71, 104–117. https://doi.org/10.1016/j.energy.2014.04.075. Pacheco, A., na, E.G., Plomaritis, T., Garel, E., Gonçalves, J., Bentes, L., Monteiro, P., Afonso, C., Oliveira, F., Soares, C., Zabel, F., Sequeira, C., 2018. Deployment characterization of a floatable tidal energy converter on a tidal channel, Ria Formosa, Portugal. Energy 158, 89–104. https://doi.org/10.1016/j.energy.2018.06.034. Popper, A.N., Fewtrell, J., Smith, M.E., McCauley, R.D., 2003. Anthropogenic sound: effects on the behavior and physiology of fishes. Mar. Technol. Soc. J. 37, 35–40. https://doi.org/10.4031/002533203787537050. Ribeiro, J., Bentes, L., Coelho, R., Gonçalves, J.M., Lino, P.G., Monteiro, P., Erzini, K., 2006. Seasonal, tidal and diurnal changes in fish assemblages in the Ria Formosa Lagoon (Portugal). Estuar. Coast. Shelf Sci. 67, 461–474. https://doi.org/10.1016/j. ecss.2005.11.036. Ribeiro, J., Monteiro, C.C., Monteiro, P., Bentes, L., Coelho, R., Gonçalves, J.M., Lino, P.G., Erzini, K., 2008. Long-term changes in fish communities of the Ria Formosa coastal lagoon (southern Portugal) based on two studies made 20 years apart. Estuar. Coast. Shelf Sci. 76, 57–68. https://doi.org/10.1016/j.ecss.2007.06.001. Rolland, R.M., Parks, S.E., Hunt, K.E., Castellote, M., Corkeron, P.J., Nowacek, D.P., Wasser, S.K., Kraus, S.D., 2012. Evidence that ship noise increases stress in right whales. Proc. R. Soc. B Biol. Sci. 279, 2363–2368. https://doi.org/10.1098/rspb. 2011.2429. Salles, P., Voulgaris, G., Aubrey, D.G., 2005. Contribution of nonlinear mechanisms in the persistence of multiple tidal inlet systems. Estuar. Coast. Shelf Sci. 65, 475–491. https://doi.org/10.1016/j.ecss.2005.06.018. Sarà, G., Dean, J.M., D’Amato, D., Buscaino, G., Oliveri, A., Genovese, S., Ferro, S., Buffa, G., Martire, M.L., Mazzola, S., 2007. Effect of boat noise on the behaviour of bluefin tuna Thunnus thynnus in the Mediterranean Sea. Mar. Ecol. Prog. Ser. 243–253. https://doi.org/10.3354/meps331243. Soares, C., Martins, C., Zabel, F., Silva, A., 2011. On the applications of a compact autonomous acoustic recorder. In: OCEANS 2011 IEEE - Spain, pp. 1–5. https://doi.org/ 10.1109/Oceans-Spain.2011.6003602. Strasberg, M., 1979. Nonacoustic noise interference in measurements of infrasonic ambient noise. J. Acous. Soc. Am. 66, 1487–1493. https://doi.org/10.1121/1.383543. Williams, B., Magar, V., 2015. Event-parallel simulation of medium-term morphodynamic evolution within a mixed energy tidal inlet ebb delta. In: Coastal Sediments 2015, https://doi.org/10.1142/97898146899770130. Williams, R., Trites, A.W., Bain, D.E., 2002. Behavioural responses of killer whales (Orcinus orca) to whale-watching boats: opportunistic observations and experimental approaches. J. Zool. 256, 255–270. https://doi.org/10.1017/S0952836902000298. Wright, A.J., Deak, T., Parsons, E., 2011. Size matters: management of stress responses and chronic stress in beaked whales and other marine mammals may require larger exclusion zones. Mar. Pollut. Bull. 63, 5–9. https://doi.org/10.1016/j.marpolbul. 2009.11.024. Cetaceans and military sonar: a need for better management.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The paper is a contribution to the SCORE project, funded by the Portuguese Foundation for Science and Technology (FCT - PTDC/AAGTEC/1710/2014). André Pacheco orcid="0000-0002-8340-0629" was supported by the Portuguese Foundation for Science and Technology under the Portuguese Researchers' Programme 2014 entitled “Exploring new concepts for extracting energy from tides” ( IF/00286/2014/ CP1234). Eduardo González-Gorbeña orcid="0000-0003-1996-7820" has received funding for the OpTiCA project from the Marie Sklodowska-Curie Actions of the European Union's H2020-MSCA-IF-EFRI-2016 /under REA grant agreement no. [ 748747]. References
André, M., Solé, M., Lenoir, M., Durfort, M., Quero, C., Mas, A., Lombarte, A., van der Schaar, M., López-Bejar, M., Morell, M., Zaugg, S., Houégnigan, L., 2011. Low-frequency sounds induce acoustic trauma in cephalopods. Front. Ecol. Environ. 9, 489–493. https://doi.org/10.1890/100124. Bassett, C., Thomson, J., Dahl, P.H., Polagye, B., 2014. Flow-noise and turbulence in two tidal channels. The Journal of the Acoustical Society of America 135, 1764–1774. https://doi.org/10.1121/1.4867360. Christiansen, F., Lusseau, D., Stensland, E., Berggren, P., 2010. Effects of tourist boats on the behaviour of Indo-Pacific bottlenose dolphins off the south coast of Zanzibar. Endanger. Species Res. 11, 91–99. https://doi.org/10.3354/esr00265. Erbe, C., 2002. Underwater noise of whale-watching boats and potential effects on killer whales (Orcinus orca), based on an acoustic impact model. Mar. Mammal Sci. 18, 394–418. https://doi.org/10.1111/j.1748-7692.2002.tb01045.x. González-Gorbeña, E., Pacheco, A., Plomaritis, T.A., Ferreira, O., Sequeira, C., 2018. Estimating the optimum size of a tidal array at a multi-inlet system considering environmental and performance constraints. Appl. Energy 232, 292–311. https://doi. org/10.1016/j.apenergy.2018.09.204. Hatch, L., Clark, C., Merrick, R., Van Parijs, S., Ponirakis, D., Schwehr, K., Thompson, M., Wiley, D., 2008. Characterizing the relative contributions of large vessels to total ocean noise fields: a case study using the Gerry E. Studds Stellwagen Bank national
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Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Baseline
Baseline assessment of underwater noise in the Ria Formosa C. Soaresa,*, A. Pachecob, F. Zabela, E. González-Goberñab, C. Sequeirab a b
MarSensing Lda., Centro Empresarial Pavilhão B1, Campus de Gambelas, 8005-139 Faro, Portugal CIMA, University of Algarve Ed7, Campus de Gambelas, Faro 8005-139, Portugal
A R T I C LE I N FO
A B S T R A C T
Keywords: Underwater noise Anthropogenic noise Noise monitoring Soundscape Ship noise
The Ria Formosa is a sheltered large coastal lagoon located on the Atlantic South Coast of Portugal, that has been classified as a natural park since 1987. The lagoon hosts a diverse and abundant fish community and other species of commercial importance. Several economical activities are supported by shipping, and as such, vessel traffic within the Ria Formosa lagoon is very intense at some locations during particular seasons of the year, creating high levels of underwater noise. Recently, strong efforts are being made to turn the main inlet of the lagoon, the Faro-Olhão Inlet, a testing site for small scale tidal stream turbines, which will bring an additional source of underwater noise. Underwater noise can be one of a number of factors causing habitat degradation, as it can perturb fish behavior and cause physiological damage. Therefore, in order to comply with underwater noise pollution regulations, tidal energy technology developers are very interested in minimising the introduction of acoustic energy in the environment during the operation of their devices. Under the scope of project SCORE, which involved the deployment and operation of a floating tidal energy converter, this paper presents and discusses the first baseline noise monitoring performed at Ria Formosa. The acoustic data were collected in two occasions over several days, one in the winter and the other in the summer, in 2017. The obtained analysis results highlight the potential impact of the intense boat traffic in Ria Formosa, and the wide range of sound levels introduced in that ecosystem, and the high diurnal and seasonal variability.
1. Introduction
2012), and changes in behavior (Nowacek et al., 2007) including evasive tactics (Williams et al., 2002; Christiansen et al., 2010); and temporary or permanent damage in auditory systems of marine mammals and fish due to short or long-term exposure to anthropogenic noise. While the awareness on the noise effects was initially focused on marine mammals, as a consequence of a series of cetacean strandings in the 1990s, there has been an increasing concern regarding the effects of anthropogenic sounds on fishes (Popper et al., 2003) and marine invertebrates (André et al., 2011), since the inner ear hearing receptor of fishes are similar to those of marine mammals. In a study near Sicily, Sarà et al., observed the disruption of schools of bluefin tuna each time a passenger ferry approached (Sarà et al., 2007). While most studies on anthropogenic underwater noise focused in coastal and deep water areas, this study characterises baseline noise levels at an inshore location in Ria Formosa. Ria Formosa is a sheltered mesotidal coastal lagoon, located on the Atlantic south coast of Portugal. Shallow inshore areas such as coastal lagoons are highly productive, capable of sustaining great diversity and densities of organisms. The Ria Formosa lagoon supports a diverse and abundant fish community, with significant populations of juveniles of many commercially important fish species. It has been recognised as an important
During the last three decades, scientific and environmental organisations have been increasingly concerned with the problem of underwater noise as a form of pollution. Different sources contribute to the total noise budget in different marine areas. Shipping is considered the most significant contributor at the global scale (Hatch et al., 2008; McKenna et al., 2012). In some ocean locations, seismic exploration conducted by means of air guns can render extremely high noise levels for a variable extension of time. At a regional or local scale, local vessel traffic supporting fisheries and a number of other activities may be the most significant contributor to anthropogenic noise levels. More recently, with the advent of the offshore renewable energy, an additional type of noise source is potentially becoming increasingly significant, where usually distinct phases are considered — construction or deployment; operation; and decommissioning. Most of these noise sources are chronic, whose cumulative contributions represent anthropogenic pressures that may lead to habitat degradation. There are different known effects on marine animals, including masking of biologically relevant sounds (Erbe, 2002; Jensen et al., 2009), physiological stress (Wright et al., 2011; Rolland et al., ∗
Corresponding author E-mail address: [email protected] (C. Soares).
https://doi.org/10.1016/j.marpolbul.2019.110731 Received 16 June 2019; Received in revised form 6 November 2019; Accepted 11 November 2019 0025-326X/ © 2019 Elsevier Ltd. All rights reserved.
Please cite this article as: C. Soares, et al., Marine Pollution Bulletin, https://doi.org/10.1016/j.marpolbul.2019.110731
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and Tavira) and one natural inlet (Armona). The large embayment located behind the barrier system is occupied by salt marshes, sand flats and a complex network of natural and partially dredged channels. Thus, it encloses natural and artificial tidal inlets, exposed sandy beaches, salt marshes, sand flats and a complex network of tidal channels. It is a protected area since 1987, with the status of Natural Park, with the aim of preserving the lagoon system, protect the fauna and flora, migratory species and habitats, and promote the orderly use of the land and the natural resources, contributing to the economic, social and cultural development. Because it is a highly productive ecosystem, of great ecological diversity, it has been recognised as very important at both European and International level by its acceptance as part of the Natura 2000 European network for nature conservation, as RAMSAR wetland and included in the Special Bird Protection Area (hosting on a regular basis more than 20,000 birds during the wintering period). The tides in the area are semi-diurnal with typical average astronomical ranges of 2.8 m for spring tides and 1.3 m for neap tides. A maximum tidal range of 3.5 m can be reached during equinoctial tides, and over 3.8 m with surge setup. Wave climate in the area is moderate (an offshore annual mean significant wave height of Hs ≈ 1 m and peak interval, Tp of 8.2 s, with storms characterised by Hs > 3 m). Approximately 71% of waves approach from W-SW, with about 23% coming from E-SE. The lagoon is generally well mixed vertically, with no evidence of persistent haline or thermal stratification; owing to the reduced freshwater inputs and elevated tidal exchanges, the lagoon is basically euryhaline with salinity values usually close to those observed in adjacent coastal waters (Newton and Mudge, 2003). The wind is on average moderate (3 ms−1) and predominantly from the west (Williams and Magar, 2015). Variance analysis of both tidal and non-tidal signals has shown that the meteorological and long-term water-level variability explains less than 1% of the total recorded variance (Salles et al., 2005). The objective of the noise monitoring procedure was to establish a baseline measurement at the site. The acoustic data were collected at the main inlet (Faro-Olhão inlet) of Ria Formosa coastal lagoon with an autonomous hydrophone, the digitalHyd SR-1, over two periods, respectively in the winter and summer seasons. Fig. 2 (a) shows the study area with deployment positions. For the first deployment, marked with ×, acoustic data collected between 20th January and 1st February 2017 is taken into account herein, and for the second deployment, marked with ◊ acoustic data collected between the 1st and 22nd August was considered. In the first occasion, the acoustic recorder was deployed at latitude N36.9749 and longitude W7.8753, at water depth of approximately 11 m (considering mean sea water level). In the second occasion, the acoustic recorder was deployed at latitude N36.9741 and longitude W7.8718, at water depth approximately 13.4 m. Fig. 2 (b) shows the hydrophone mooring, made of a robust tripod structure deployed on the seabottom. The SR-1 autonomous hydrophone is a compact acoustic recorder equipped with a SensorTech SQ26 transducer ( sensitivity is −194 dB re 1 V/1 μ Pa with a variation within ± 1dB in the 1 Hz to 28 kHz interval), driving a 32 dB pre-amplifier, and a programmable gain amplifier (PGA) with selectable gains from 1×,2×,…,64 × (Soares et al., 2011). For the actual acquisition, the PGA was set to a gain of 16 ×, rendering an overall nominal sensitivity of −143 dB re 1 V/1 μ Pa; the sampling rate was set to 52,734 sps and amplitude resolution was set to 24 bits. The acquisition was scheduled to start every 10 min for 90 s, i.e. a duty-cycle of 15%. For each acquisition interval a WAV file was generated. The actual duty-cycle was sufficient to record boats passing within the receiver's position neighborhood. The total effective acquisition time was approximately 50 h for the deployment in the winter season and 76 h for the deployment the summer season. Current velocity was sampled with an ADCP during the acoustic data acquisition carried out in January. Fig. 3 shows the absolute value of current velocity. It is seen that the velocity peaks attain about 0.4 m/ s during date 20/01, from which the peak speed progressively increases
natural wetland with great economic value, classified as a natural park since 1987 and accepted as a Ramsar and Natura 2000 site. The lagoon supports many human activities, including tourism, aquaculture, shipping, fishing, harvesting of bait, salt production and sediment extraction (Ribeiro et al., 2006, 2008). Most of these activities are supported by the local vessel traffic, consisting of fisher boats, and passenger and recreational boats, and other small boats with inboard or outboard engines, and sailing boats underway with engines. The local vessel traffic is very intense in some areas of Ria Formosa over some seasons of the year, rising concerns on the effects caused by the introduction of noise on the local fauna. Most acoustic energy radiated by small boats with outboard or inboard engines has its spectral contents in the frequency range 0.1 to 5 kHz and source level 150 to 180 dB re 1 μ Pa (Hildebrand, 2009). These sounds are within the hearing range of many fish species, which often have best hearing sensitivity at frequencies below 1 kHz (Popper et al., 2003). Another relevant activity that has the potential to contribute for further increments of the noise level in Ria Formosa is tidal energy exploration. The Faro-Olhão Inlet, which is responsible for exchanging the largest volumes of water between the ocean and the lagoon, has a significant tidal stream energy resource (Pacheco et al., 2014; González et al., 2018). Under the SCORE project1, a 1:10 scale floatable tidal energy converter (TEC) prototype (Evopod E1 from Oceanflow Energy Ltd.) was deployed and operated for 5 months constituting the first time that a device of this characteristics was tested in Portuguese waters. The project outcomes proved the potential of Faro-Olhhão Inlet to become into a testing site for small TECs technologies (Pacheco et al., 2018). Project MARINERG-i is developing a scientific and business plan for an integrated European Research Infrastructure network in offshore marine renewable energy, which provides technology developers a network of testing sites (Marinerg-I Project, 2019). In the European Union (EU), most of the sites to test TECs are in the United Kingdom waters. With Brexit, the access to these facilities through EU funds is uncertain, which may constitute a serious drawback for the evolution of the tidal energy sector. Moreover, tidal stream energy is very site specific, this means that suitable regions for testing are not as common as for wave energy and offshore wind. Therefore, identifying and fully characterising new sites in EU waters, such as Faro-Olhão Inlet, is of vital importance to ensure the competitiveness of EU's ocean energy sector. However, in order to turn it into a recognised site and promote it to be included as part of the MARINERG-i research infrastructure network, a full and detailed characterisation of the site is needed. Establishing current baseline levels contribute to characterise the acoustic levels of the region and will allow tidal energy developers to assess the contribution of their technologies to noise pollution, providing valuable information to optimise their designs to mitigate this type of undesirable impact. The baseline study presented in this paper was conducted in the scope of the SCORE project (Pacheco et al., 2018). To our best knowledge, this is the first underwater noise monitoring ever conducted in the Faro-Olhão Inlet of Ria Formosa coastal lagoon. The paper introduces the main characteristics of the study region; describes the methodology approach, including aspects of the acoustic acquisition setup and data analysis; then presents the experimental results; and finally presents the main conclusions. The noise monitoring was conducted at Faro-Olhão Inlet, the main inlet of Ria Formosa system (hereafter RF), a coastal lagoon located in the South of Portugal (Fig. 1). The RF is one special location along European coastlines since is, with Wadden Sea, the two only multiple inlet systems of European coasts. It has about 18,400 ha, and 60 km of extension, presently comprising six islands and one peninsula separated by seven tidal inlets: four artificially opened or relocated (Ancão, Fuseta, Lacém and Cacela), two artificially stabilised inlets (Faro-Olhão
1 SCORE stands for Sustainability of using Ria Formosa Currents On Renewable Energy production.
2
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Fig. 1. Deployment site adjacent to Faro-Olhão Inlet (A), main inlet of Ria Formosa lagoon system (Algarve, Portugal), for future test site of tidal energy converters (B). Fig. 2. Acoustic recordings experimental setup at the Ria Formosa inlet. (a) work box with autonomous recorder deployment positions: January 2017 (×); August 2017 (◊). (b) Autonomous recorder mounted on a tripod.
Fig. 3. Current velocity as function of time during the acoustic acquisition period from 20th of January to 1st of February.
intervals of the day. The data indicates that wind was more intense during the day than during the night, over the relevant observation interval, attaining the maximum average speed during times 09:00 to 12:00. During the acoustic data acquisition carried out in August, no wind speed was sampled.
to 1.1 m/s at the end of the interval. During the acoustic data acquisition carried out in August no current velocity was sampled. Wind speed data was made available for the interval from 20th January to 1st February, whereas wind speed was sampled every 30 minutes at the Faro airport. Table 1 shows average wind speed over 3
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corresponding to the data acquired in January 2017. The spectrograms were obtained by averaging periodograms of 4096 samples segments to 90 s resolution via the Welch method, i.e. the total data portion acquired every 10 min. Each spectrogram is completed with the broadband level in dB re 1 μPa2 over time, integrating the acoustic power from 35 Hz to 22,449 Hz, merging the frequency coverage of all 1/3 octave bands considered in the analysis reported below. Recall that the site of deployment was in the main inlet close to a traffic route leaving or entering the Ria Formosa, and therefore idle and busy intervals of traffic were expected a priori to occur. In January there are effectively idle periods, with no boats passing during the nights of 22/01, 29/01 (Saturday to Sunday) and 23/01 (Sunday to Monday). During several other nights few discrete acoustic events related to boat traffic are noticed, where broadband levels as low as 87 dB re 1 μPa are observed. When boats cruise the area, broadband levels reach approximately 110 dB re 1μPa and peak above 120 dB re 1μPa few times. During the second week some periods with a broadband noise becomes visible, during intervals where current speed exceeds 0.8 m/s. Current speed may cause flow-noise or pseudosound, as a result of both advected ambient turbulence and interactions with the transducer in the flow, which will increase the acoustic pressure received at the transducer. This is seen as an interference rather than noise that was actually radiated by a source away from the transducer (Bassett et al., 2014; Strasberg, 1979). In the absence of discrete events during the night from 21st to 22nd of January, it is possible to observe a slow variability in the level of background noise. It is apparent that about midnight the level of background noise is maximized and after it progressively reduces until the first discrete events occur at time 05:15. This observation is in agreement with the peak in current velocity of 0.35 m/s attained just after midnight and the minimum speed achieved at time 04:45. Also over the night from 22nd to 23rd January a similar phenomena is observed where a maximum of 0.3 m/s in current velocity occurs before midnight and another peak of 0.35 m/s occurs after midnight, and a minimum velocity at about midnight. However, this relation was not always found. Further inspection on the contribution of current velocity on the measured noise level indicated that a significant contribution is seen when it exceeds 0.8 m/s. During the interval from 20th to 25th January current speed peaked between 0.25 and 0.7 m/s, and from 26th January to 1st February current speed peaked between at 0.2 and at 1.1 m/s.
Table 1 Average wind speed from 20th January to 1st February over intervals of 4 h. Time (hh:mm)
Wind speed (m/s)
00:00 04:00 08:00 12:00 16:00 20:00
3.9 4.1 4.9 4.6 3.6 3.6
to to to to to to
04:00 08:00 12:00 16:00 20:00 24:00
The acoustic data were processed in MATLAB using custom-written scripts. First a spectrogram was computed for each WAV file, computed by means of the Discrete Fourier Transform with a Hanning window of 4096 samples (equivalent to approximately 0.078 s), which yields 1185 instant frequency observations for each file. Each spectrogram was saved in its respective file for subsequent analysis. The subsequent analysis consisted of a statistical analysis both on broadband and 1/3 octave bands acoustic pressure. The objective was to characterise the soundscape based on indicators such as mean levels and percentiles as to obtain level exceedance in proportion of time, and spectral probability densities (SPD) (Merchant et al., 2013). Historically, the root of mean squared (rms) level is the most prevalent metric. However, it is strongly influenced by the highest instant sound levels and should be used carefully with transient sound sources (i.e. such as boats passing occasionally at the measurement position) (Merchant et al., 2012). The 1/3 octave bands use standard central frequencies, where the first band is centred at 39 Hz and a 9 Hz bandwidth; and the last band has 20 kHz central frequency and a 4631 Hz bandwidth. The width of each band is directly proportional to the centre frequency. The spectral slope of the fractional octave band levels differs from the power spectral density (PSD), as the bands are scaled logarithmically with frequency, meaning that the bands widen with increasing frequency. Therefore, higher frequency bands integrate energy over larger frequency ranges. The analysis of a time series can provide a broad idea on discrete events and temporal patterns. The spectrogram is widely used in time series analysis, as it allows for the observation of variations of the sound level with time over the frequency band. Visual inspection allows for the detection or even identification of discrete sound sources such as boats passing near or vocalisations of animals, since each type of sound source may show distinct spectral contents. Fig. 4 shows spectrograms
Fig. 4. Time series analysis of acoustic data acquired with an autonomous hydrophone in January 2017: 20th to 25th January (top panel); and 26th January to 1st February (bottom panel). The analysis has been performed using segments of 4096 samples (≈0.078 s) averaged to 90 s using the Welch method. 4
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Fig. 5. Time series analysis of acoustic data acquired with an autonomous hydrophone in August 2017: 2nd to 8th August (top panel); 9th to 14th August (middle panel); and 15th August to 21st August (bottom panel). The analysis has been performed using segments of 4096 samples (≈0.078 s) averaged to 90 s using the Welch method.
floor seen in January is taken as the reference background noise, then the perturbation caused by a boat passing in this channel is able to exceed the natural background noise by 40 dB or more, which can be considered dramatic. The visual analysis clearly suggests that there are two regimes in both data sets: a diurnal regime where the soundscape is contaminated to large extent by the local boat traffic; a night regime with reduced or inexisting boat traffic. In order to determine more exactly which are the busy and idle intervals in terms of noise level, a statistical analysis on 1/ 3 octave bands spectra has been carried. Fig. 6 shows percentile 50 obtained for 1/3 octave bands bins over 1-hour intervals for the complete acquisition periods in the winter (left panel) and in the summer (right panel). Time is local time. Instantaneous 1/3 octave bands spectra were computed from the FFTs (no averaging), and percentile 50 obtained for each 1-hour interval over all days comprised in each acquisition period. Percentile 50 indicates the level that is exceeded during 50% of the time. This analysis allows for discriminating the idle interval from the busy interval almost free of ambiguity, since there is a step of 10 to 15 dB in the intermediate region in the direction of the time axis (left panel), comprising almost the entire frequency band analysed. This somewhat ad-hoc criteria led to the decision that the busy interval is from times 07h00 to 17h00 of the day, and the rest of the time is the idle interval, in both data sets. The possibility of using Automatic Identification System (AIS) data was considered, but it was concluded that it would be of little use since most boats passing near are recreational or small fisher boats not equipped with that system, and hence these data would be not representative of the actual traffic in the working area. Two different regimes were identified on both data sets, an idle and a busy regime, as the result of diverse degrees of vessel traffic intensity at day and night, at weekdays and weekends, and in the winter and summer seasons. In opposition to the time series representation, which is more suited to describe events and trends in sound levels, now a statistical analysis is carried out to fully characterise the observed soundscape (Merchant et al., 2015), by taking into account the two
Wind measurements show that, in average, higher wind speed occurred during the day, ranging from 4.6 to 4.9 m/s (see Table 1). During the night average wind speeds from 3.6 to 3.9 m/s were observed. Similar values are observed when only diurnal and nocturnal periods are considered. The visual analysis of the spectrograms does not provide a discrimination of significant contributions of windspeed to the received noise level, as the noise floor tends to be lower during the day. In other words, in the January data set, the noise floor during the day is lower than at night, despite the normal increase of boat traffic at daytime. Several natural sounds of transient nature can be heard continuously, possibly caused by organisms contributing for increased natural background noise during the night. Fig. 5 shows spectrograms corresponding to the data acquired in August 2017. By comparing this time series with that of January, it becomes quite apparent that the two time series correspond to two different soundscapes, although with the same underlying phenomena — the local vessel traffic. In both cases, it is seen that there are busy intervals alternated with idle or relatively idle periods, centred at midnight. The soundscape in August is more consistently contaminated with noise radiated by boats, and peak values are more consistently reached and maintained, as sound level often approach or exceeds 120 dB re 1μPa level consistently, while in the soundscape in January the discrete noise events are less overlapped. In August, the spectral power has significantly increased over the entire frequency range. The minimum noise level is observed around times 05/08 (Saturday to Sunday), and 06/08 (Sunday to Monday), and around time 13/08, although with a few boat passages observed, hence not completely free of boat noise. This comparison also shows that the noise patterns in January and August are significantly different due to increased vessel traffic intensity in the summer months. While in January the broadband level is characterised by peaks that tend to be time discrete and discriminable in many occasions in August the events overlap and therefore the broadband level tends to remain sustained during the whole day with a smooth and progressive variation at dawn and evening. If the noise
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Fig. 6. Percentile 50 (median) for 1/3 octave noise levels for each hour of the day over the entire acoustic acquisition interval in each season: winter season in January 2017 (left); summer season in August 2017 (right).
be seen as the weighted average between the busy and idle regimes. Another feature to consider is that the curves show a relatively long tail after the 5% mark, which includes observations exceeding the 5% mark by more than 25 dB. This is presumably caused by boats that passing near the hydrophone, which become statistical outliers in the acoustic data set. The outcome of the comparative result between the busy and idle intervals comes from the fact that the noise events are discrete in time with relatively short transient times, and with levels outstanding significantly from ambient noise. The summer data shows a different behavior, concerning the comparison between the two periods. All curves are clearly separated with no overlapping. In this case, the sound level variability is higher in the idle interval than in the busy interval, as the 95% marks are about 7 dB apart, and the 5% marks are only 5 dB apart. This agrees with the fact that noise level is sustained during the busy interval, showing the progressive increase/reduction of local boat traffic at the beginning/ end of the day, respectively. Table 2 summarizes the statistical results on broadband SPL by means of percentiles and rms levels for the three analysed intervals. The percentiles reflect the information given in the form of level exceedance in proportion of time presented in Fig. 7. The results show that the actual site is subject to quite intense noise levels. It seems to be acceptable to take percentile 1 as a reference for the natural sound level, because no anthropogenic noise source was noticed during three nights in January, which exceeds more than 1% of the total observation time. It is assumed that during such intervals the natural ambient noise level was stationary, without statistically significant variability from natural sources (e.g. weather, tidal processes, soniferous fish and invertebrates). Hence, if percentile 1 is taken as the natural sound level, which would be about 84 dB in the winter, then the level increment over significant time intervals can exceed approximately 41 dB during 1% of the observation time in the summer season. It is also shown that while in the winter the idle to busy rms SPL ratio is about 4 dB, in the summer this ratio is about 9 to 10 dB. As a complementary analysis in the time domain, and to assess the diel variability of BB SPL, Fig. 8 shows boxplots for 1 hour intervals over the entire acquisition period, for the winter data set (left panel) and the summer data set (right panel). The black mark is the median,
daily intervals, over the two observation periods. In order to establish the baseline for the actual area, first broadband noise levels are obtained, and then a frequency analysis is provided, in terms of significant variability and distribution. Next, an analysis on broadband noise levels is provided. As stated above, broadband level refers to the sound level integrating all power contained in the frequency band from 35 Hz to 22,449 Hz, which is the range of frequencies covered from the lower frequency bound of the first 1/3 octave band to the upper frequency bound in the last 1/3 octave band considered herein. In this way, coherence between the broadband sound pressure level (SPL) and 1/3 octave level analyses is maintained. Fig. 7 shows Level Exceedance in Proportion of Time for the two complete data sets. To estimate the level exceedance for a given proportion of time, the instantaneous SPL observations obtained from data windowing within a given interval of the day were collected and ranked in ascending order as to extract the percentiles from zero to 100 with a resolution of 1. Then the percentile was simply reversed for conversion to percent — the proportion of time. For example, percentile 5 corresponds to the SPL exceeded during 95% of the time. The curve obtained by this processing would be similar to that obtained by computing the cumulative distribution of the samples, in particular, for a large number of realizations. This analysis was carried out for the two comparison intervals, from times 7h00 to 17h00 (blue curve), and from times 17h00 to 07h00 (black curve), and for the full acquisition time (gray curve). For comparison, markers at proportions 95% and 5% of the time have been included. The panel on the left of Fig. 7 corresponds to the winter data, and the panel on the right corresponds to the summer data. Considering the winter data first, it is seen that the variability over the diurnal period is higher then over nocturnal period, since the blue and black curves cross at 80% of time, which confirms that during the busy interval the noise floor is lower that during the idle interval. Towards the upper end, the results indicate that the diurnal period (busy interval) is louder than the nocturnal period. The level exceedance for 5% of the time is 112 dB re 1μPa for the diurnal period (blue), 107 dB re 1μPa for the nocturnal period (black). The gray curve comprises the complete data set, and therefore shows a result that can
Fig. 7. Broadband sound level exceedance in proportion of time: January 2017 (left); August 2017 (right). Busy interval is from 07h00 to 17h00 (blue); idle interval is from 17h00 to 07h00 (black); all data (gray). The circle markers indicate level exceedance for 95% and 5% of the time. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Table 2 Broadband sound pressure level: statistical analysis summary for the acoustic data collected in the two seasons, over the three intervals. All day comprises all data; diurnal comprised all data collected during day, from 07h00 to 17h00 local time; nocturnal comprised all data collected during night, from 17h00 to 07h00 local time. Percentiles pn and rms pressure values are given in dB re 1 μPa. Season
Day/night
p1
p5
p25
p50
p75
p95
p99
rms level
Winter
All day Diurnal Nocturnal All day Diurnal Nocturnal
84.4 83.8 84.9 90.0 94.1 89.5
86.5 86.0 86.9 92.1 98.6 91.3
89.7 90.3 89.6 96.5 105.4 94.5
92.6 95.9 91.6 103.7 110.0 98.0
98.0 102.4 95.0 110.7 114.1 104.8
109.1 112.0 105.0 118.0 120.0 114.8
117.1 120.8 112.8 123.3 125.1 121.0
94.7 97.0 93.1 104.0 109.7 100.2
Summer
the box edges are the first and third quartiles, and the blue circles indicate percentiles 1 and 99 (whiskers). This result describes the dependence of the noise level with time of the day, as a result of the boat traffic nearby. In both data sets, there is a high degree of skewness for times in the idle interval, where the summer data shows a slightly lower asymmetry for that period, due to longer interquartile ranges. During the busy interval, from times 07h00 to 17h00, the statistical behavior in both cases can be distinguished as follows: in the winter data, percentile 25 remains nearly constant at all time, and there is an evident increase in the interquartile range during the busy interval, but skewness remains. In the summer data, there is an onset of the box in the centre of the significant interval, rendering a nearly symmetric distribution, and the interquartile range is slightly reduced in comparison to the idle interval, except for time from 19h00 to 01h00. Concerning the whiskers, the behavior is distinct too: in the winter data set percentile 1 progressively reduces during the day, reaching the minimum at time 14h00, while percentile 99 increases significantly in comparison to the idle interval; in the summer data the whiskers show two distinct periods, i.e. percentile 1 shows an onset to 90 dB from time 20h00 to time 10h00, and a variation between 95 and 100 dB from time 11h00 to time 19h00, which indicates a change from discrete acoustic events to sustained or overlapping acoustic events. The winter data shows an outlier for percentile 99 at time 12h00, whose origin can be spot in the time series of Fig. 4, where an increased transient level is seen at about 12h00 at days 22nd, 23rd, and 25th of January. The actual results on the broadband noise levels shown here provide a thorough quantification of the features seen in the time series analysis above, both over different time intervals and the complete interval of each data set. The statistical analysis carried out for the broadband levels can be applied in the frequency domain for the assessment of the variability over significant spectral bands. In the actual case, this analysis provides an idea on the acoustic contamination introduced by the noise sources over the observed frequency band, by comparing the busy and idle regimes. Fig. 9 shows boxplots for the two data sets over 1/3 octave bands, with the winter data set on the left column, and the summer data set on right column. The frequencies are the centre frequencies of the 1/ 3 octave bands, whose width increases logarithmically with frequency. This should be accounted for, since the plotted amplitudes represent the power integration over the respective 1/3 octave band.
In general, the boxplots indicate that the levels in bands with centre frequencies up to 78 Hz, and about 5 kHz, received reduced influence from the boat traffic, since the increase from idle to busy interval, or from winter to summer is relatively reduced. Towards the lower end of the band, those bands show symmetric distributions, and towards the higher end the skewness progressively increases. Above the 78 Hz band, the increment in sound level is more noticeable when comparing idle to busy interval, and winter to summer season. The distribution symmetry increases from winter to summer data set, in particular, in the bands from 98 to 5 kHz, indicating that in this band the noise contamination during the summer is sustained. Within this band, when comparing the idle interval in winter to busy interval in summer, the increase in level is approximately 15 dB, in all bands. The boxplot analysis for the winter data set shows that percentile 1 and percentile 25 are lower in the busy interval than in idle interval for frequencies above 1575 Hz and 1984 Hz, respectively, which gives an insight on the minimum noise floor shown in Fig. 8 (left panel). The increased high-frequency content during the idle interval could be of biotic origin, from organisms such as shrimps and sea fleas with increased acoustic activity during the night. The bottom boxplots of Fig. 9 are for all data, and is therefore a combination of the other two obtained for idle and busy intervals. Fig. 10 shows the spectral probability density (SPD) (Merchant et al., 2013), whereby the empirical probability of sound levels as a function of frequency is presented, with superimposed percentile curves across the frequency band for percentiles 1, 5, 50, 95 and 99. The left and right panel correspond to the data sets collected in January 2017 and August 2017, respectively. Only complete data sets are shown for this indicator. This analysis can reveal multi-modality in the received sound level. In actual data sets, the observed noise level has a unimodal characteristic, although two very distinct regimes were identified throughout the analysis of these data sets. It can be seen that while for the winter data the distribution tends to be concentrated around the median, for the summer data the level distribution peak has shifted significantly towards higher levels, for frequencies between 100 and 1000 Hz, and for frequencies above 1000 Hz the level is more spread over the SPL interval. In the later case the sample realizations suffer increased spreading across the sound level interval. These empirical distributions complement the frequency boxplot as it clearly reveals how the noise levels are distributed over narrow
Fig. 8. Boxplot for broadband SPL over 1 hour intervals of the acoustic data acquired from 20th January to 1st February. The black mark is the median, box edges are the first and third quartiles, and the blue circles indicate percentiles 1 and 99. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 7
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Fig. 9. Boxplot of 1/3 octave bands for data sets collected in January (left) and August (right). The analysis is divided in nocturnal interval (top); diurnal interval (middle); and all time (bottom). The black mark is the median, box edges are the first and third quartiles, and the blue circles indicate percentiles 1 and 99 (whiskers). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Acoustic data has been acquired at an inlet of Ria Formosa coastal lagoon with an autonomous hydrophone moored by means of a tripod structure deployed on the seabottom at an average water depth of approximately 11 m, in two occasions in 2017, for 12 days in January, and for 21 days in August. The deployment site is characterised by high flow velocities due to tidal cycles and serves as passage of local boats. Due to fisher and touristic activities, the traffic intensity is very distinct from winter to summer season, and therefore distinct degrees of acoustic contamination was expected to be found. The acoustic data analysis was based on statistical processing on the broadband sound level, 1/3 octave bands, and FFT bins. In a first analysis step, by means of the estimation of the median of 1/3 octave band levels over 1 hour intervals, two intervals of acoustic intensity were established, as a result of distinct traffic intensity, which were called idle and busy intervals. Then data analysis was performed as to separate these periods. This separation proved to be important in maintaining the ability to estimate reference levels of the natural noise. The results indicate very distinct soundscapes from night to day, and from winter to summer seasons, as a results of the increased boat traffic in the summer. The mean broadband level in the summer is more than 9 dB higher than that of the winter. If percentile 1 in the winter is used to estimate the level of natural noise, than percentile 1 in the summer is increased by 5 dB (for all time), and during the day this level is exceeded by more than 40 dB during 1% of the time. The frequency analysis shows that the anthropogenic acoustic contamination is most severe in the frequency band 100 to 2000 Hz, since this was the interval with highest idle to busy interval ratios. And also for the reason that in the winter data the noise floor remained
frequency bands of equal width. For example, the noise in the frequency band above 2 kHz is more compact than below that frequency. The comparison of the two SPDs reveals that the increased vessel traffic in the summer season has most contributions within the 2 kHz band, but some increased spreading above that frequency is observed. The SPL interval goes well beyond the 1% and 99% percentile boundaries over most of the frequency band, indicating occurrence of outliers, in either case. Having outliers below percentile 1 is a good indication that the acoustic recorder self-noise did not limit the noise floor. Also, the degree of skewness over the frequency band can be observed. The skewness indicates on which side of the distribution is the tail. In the actual context, it provides an idea on whether the most frequent noise levels are on the lower side or on upper side of the distribution. Since these distributions are unimodal across frequency, herein, it is useful to interpret skewness in order to determine in which interval of frequencies the change between the two data sets was most significant. For example, in the frequency band 200 to 2000 Hz, the skewness was positive in January (left panel) but it was negative in August (right panel). This is a relevant change revealing that over that frequency range the noise level increment was most significant, while in the remaining frequency intervals the change in degree of skewness was relatively reduced. The pink curve shows an estimate of the average PSD estimated by integrating the SPL over the probability distribution at each frequency. Sound level in rms values must be analysed carefully since these values are provided in a logarithmic scale, i.e. outliers on the lower and upper end of the distribution and significantly contribute for the average level.
Fig. 10. Spectral Density Probability of noise level as a function of frequency with percentiles (black curves) and PSD (pink curve): January 2017 (left); August 2017 (right). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 8
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lower in the busy interval for frequencies above 1575 Hz, even in the presence of increased boat traffic. The actual baseline survey is the first reported noise monitoring in Ria Formosa, and highlights the potential impact of the intense boat traffic associated to fisheries and touristic activities. It also highlights the wide range of sound levels to which that ecosystem is exposed within the frequency band up to 23 kHz, and the signifcantly distinct noise levels observed over different seasons of the year, weekdays and weekends, and over night and day. Finally, with regard to tidal energy converters, noise surveys to assess the levels of acoustic energy introduced in the environment during the operation of the turbines should be conducted at night and, preferably, during the winter months. This will enable comparison with baseline conditions without the contribution of boat traffic, and to clearly identify the frequencies of noise radiated by an energy conversion device.
marine sanctuary. Environ. Manag. 42, 735–752. https://doi.org/10.1007/s00267008-9169-4. Hildebrand, J.A., 2009. Anthropogenic and natural sources of ambient noise in the ocean. Mar. Ecol. Prog. Ser. 395, 5–20. https://doi.org/10.3354/meps08353. Jensen, F.H., Bejder, L., Wahlberg, M., Soto, N.A., Johnson, M., Madsen, P.T., 2009. Vessel noise effects on delphinid communication. Mar. Ecol. Prog. Ser. 395, 161–175. https://doi.org/10.3354/meps08204. Marinerg-I Project, 2019. Planning an integrated European research infrastructure. http://www.marinerg-i.eu/. Online. McKenna, M.F., Ross, D., Wiggins, S.M., Hildebrand, J.A., 2012. Underwater radiated noise from modern commercial ships. J. Acoust. Soc. Am. 131, 92–103. https://doi. org/10.1121/1.3664100. Merchant, N.D., Barton, T.R., Thompson, P.M., Pirotta, E., Dakin, D.T., Dorocicz, J., 2013. Spectral probability density as a tool for ambient noise analysis. J. Acous. Soc. Am. 133, EL262–EL267. https://doi.org/10.1121/1.4794934. Merchant, N.D., Blondel, P., Dakin, D.T., Dorocicz, J., 2012. Averaging underwater noise levels for environmental assessment of shipping. J. Acous. Soc. Am. 132, EL343–EL349. https://doi.org/10.1121/1.4754429. Merchant, N.D., Fristrup, K.M., Johnson, M.P., Tyack, P.L., Witt, M.J., Blondel, P., Parks, S.E., 2015. Measuring acoustic habitats. Methods Ecol. Evol. 6, 257–265. https://doi. org/10.1111/2041-210X.12330. Newton, A., Mudge, S.M., 2003. Temperature and salinity regimes in a shallow, mesotidal lagoon, the Ria Formosa, Portugal. Estuar. Coast. Shelf Sci. 57 (1), 73–85. https://doi. org/10.1016/S0272-7714(02)00332-3. Nowacek, D.P., Thorne, L.H., Johnston, D.W., Tyack, P.L., 2007. Responses of cetaceans to anthropogenic noise. Mamm. Rev. 37, 81–115. https://doi.org/10.1111/j.13652907.2007.00104.x. Pacheco, A., Ferreira, O., Carballo, R., Iglesias, G., 2014. Evaluation of the production of tidal stream energy in an inlet channel by coupling field data and numerical modelling. Energy 71, 104–117. https://doi.org/10.1016/j.energy.2014.04.075. Pacheco, A., na, E.G., Plomaritis, T., Garel, E., Gonçalves, J., Bentes, L., Monteiro, P., Afonso, C., Oliveira, F., Soares, C., Zabel, F., Sequeira, C., 2018. Deployment characterization of a floatable tidal energy converter on a tidal channel, Ria Formosa, Portugal. Energy 158, 89–104. https://doi.org/10.1016/j.energy.2018.06.034. Popper, A.N., Fewtrell, J., Smith, M.E., McCauley, R.D., 2003. Anthropogenic sound: effects on the behavior and physiology of fishes. Mar. Technol. Soc. J. 37, 35–40. https://doi.org/10.4031/002533203787537050. Ribeiro, J., Bentes, L., Coelho, R., Gonçalves, J.M., Lino, P.G., Monteiro, P., Erzini, K., 2006. Seasonal, tidal and diurnal changes in fish assemblages in the Ria Formosa Lagoon (Portugal). Estuar. Coast. Shelf Sci. 67, 461–474. https://doi.org/10.1016/j. ecss.2005.11.036. Ribeiro, J., Monteiro, C.C., Monteiro, P., Bentes, L., Coelho, R., Gonçalves, J.M., Lino, P.G., Erzini, K., 2008. Long-term changes in fish communities of the Ria Formosa coastal lagoon (southern Portugal) based on two studies made 20 years apart. Estuar. Coast. Shelf Sci. 76, 57–68. https://doi.org/10.1016/j.ecss.2007.06.001. Rolland, R.M., Parks, S.E., Hunt, K.E., Castellote, M., Corkeron, P.J., Nowacek, D.P., Wasser, S.K., Kraus, S.D., 2012. Evidence that ship noise increases stress in right whales. Proc. R. Soc. B Biol. Sci. 279, 2363–2368. https://doi.org/10.1098/rspb. 2011.2429. Salles, P., Voulgaris, G., Aubrey, D.G., 2005. Contribution of nonlinear mechanisms in the persistence of multiple tidal inlet systems. Estuar. Coast. Shelf Sci. 65, 475–491. https://doi.org/10.1016/j.ecss.2005.06.018. Sarà, G., Dean, J.M., D’Amato, D., Buscaino, G., Oliveri, A., Genovese, S., Ferro, S., Buffa, G., Martire, M.L., Mazzola, S., 2007. Effect of boat noise on the behaviour of bluefin tuna Thunnus thynnus in the Mediterranean Sea. Mar. Ecol. Prog. Ser. 243–253. https://doi.org/10.3354/meps331243. Soares, C., Martins, C., Zabel, F., Silva, A., 2011. On the applications of a compact autonomous acoustic recorder. In: OCEANS 2011 IEEE - Spain, pp. 1–5. https://doi.org/ 10.1109/Oceans-Spain.2011.6003602. Strasberg, M., 1979. Nonacoustic noise interference in measurements of infrasonic ambient noise. J. Acous. Soc. Am. 66, 1487–1493. https://doi.org/10.1121/1.383543. Williams, B., Magar, V., 2015. Event-parallel simulation of medium-term morphodynamic evolution within a mixed energy tidal inlet ebb delta. In: Coastal Sediments 2015, https://doi.org/10.1142/97898146899770130. Williams, R., Trites, A.W., Bain, D.E., 2002. Behavioural responses of killer whales (Orcinus orca) to whale-watching boats: opportunistic observations and experimental approaches. J. Zool. 256, 255–270. https://doi.org/10.1017/S0952836902000298. Wright, A.J., Deak, T., Parsons, E., 2011. Size matters: management of stress responses and chronic stress in beaked whales and other marine mammals may require larger exclusion zones. Mar. Pollut. Bull. 63, 5–9. https://doi.org/10.1016/j.marpolbul. 2009.11.024. Cetaceans and military sonar: a need for better management.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The paper is a contribution to the SCORE project, funded by the Portuguese Foundation for Science and Technology (FCT - PTDC/AAGTEC/1710/2014). André Pacheco orcid="0000-0002-8340-0629" was supported by the Portuguese Foundation for Science and Technology under the Portuguese Researchers' Programme 2014 entitled “Exploring new concepts for extracting energy from tides” ( IF/00286/2014/ CP1234). Eduardo González-Gorbeña orcid="0000-0003-1996-7820" has received funding for the OpTiCA project from the Marie Sklodowska-Curie Actions of the European Union's H2020-MSCA-IF-EFRI-2016 /under REA grant agreement no. [ 748747]. References
André, M., Solé, M., Lenoir, M., Durfort, M., Quero, C., Mas, A., Lombarte, A., van der Schaar, M., López-Bejar, M., Morell, M., Zaugg, S., Houégnigan, L., 2011. Low-frequency sounds induce acoustic trauma in cephalopods. Front. Ecol. Environ. 9, 489–493. https://doi.org/10.1890/100124. Bassett, C., Thomson, J., Dahl, P.H., Polagye, B., 2014. Flow-noise and turbulence in two tidal channels. The Journal of the Acoustical Society of America 135, 1764–1774. https://doi.org/10.1121/1.4867360. Christiansen, F., Lusseau, D., Stensland, E., Berggren, P., 2010. Effects of tourist boats on the behaviour of Indo-Pacific bottlenose dolphins off the south coast of Zanzibar. Endanger. Species Res. 11, 91–99. https://doi.org/10.3354/esr00265. Erbe, C., 2002. Underwater noise of whale-watching boats and potential effects on killer whales (Orcinus orca), based on an acoustic impact model. Mar. Mammal Sci. 18, 394–418. https://doi.org/10.1111/j.1748-7692.2002.tb01045.x. González-Gorbeña, E., Pacheco, A., Plomaritis, T.A., Ferreira, O., Sequeira, C., 2018. Estimating the optimum size of a tidal array at a multi-inlet system considering environmental and performance constraints. Appl. Energy 232, 292–311. https://doi. org/10.1016/j.apenergy.2018.09.204. Hatch, L., Clark, C., Merrick, R., Van Parijs, S., Ponirakis, D., Schwehr, K., Thompson, M., Wiley, D., 2008. Characterizing the relative contributions of large vessels to total ocean noise fields: a case study using the Gerry E. Studds Stellwagen Bank national
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