Ship traffic noise distribution in the Polish Baltic waters results of BIAS EU project

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1 10th EAA INTERNATIONAL SYMPOSIUM ON HYDROACOUSTICS XXXIII Symposium on Hydroacoustics, May 17-20, 2016, Jastrzębia Góra, Poland Ship traffic noise distribution in the Polish Baltic waters results of BIAS EU project Jarosław Tęgowski 1, Thomas Folegot 2, Radomił Koza 1, Karolina Trzcińska 1, Iwona Pawliczka 1, Jakub Zdroik 1, Krzysztof Skóra 1 1 Institute of Oceanography, University of Gdansk, al. Marszałka Piłsudskiego Gdynia, Poland 2 QUIET-OCEANS, 65 Place Nicolas Copernic, Plouzané, France

2 Since World War II, the average level of the ambient noise increased by db (Bjørnø, 1998) Bjørnø, L., 1998, Man-made contributions to ambient noise in the seas. Alippi, A. and Cannelli, G. B. Proceedings of the Fourth European Conference on Underwater Acoustics, Rome 2,

3 The hearing ranges of different kinds of fish and mammals together with the overlap in frequency with different sources of human-generated noise from Slabbekoorn et al. (2010)

4 Baltic Sea Information on the Acoustic Soundscape - BIAS EU-Life+ project ( ) Goals: Implementation of MSFD Descriptor 11 in the Baltic Sea region Establishing a baseline for continuous sound by measurements and soundscape modeling Establishing of a joint management, for e.g. handling of data and registry standards Developing tools for handling of Descriptor MSFD - Marine Strategy Framework Directive

5 What the EC (LIFE +) is expected from the BIAS project? What will be the main result of the project? BIAS - brochure

6 POSITIONS OF 38 ACOUSTIC DATA LOGGERS DEPLOYED IN THE BALTIC SEA Swedish Defence Research Agency (FOI) Finnish Environment Institute (SYKE) Tallinn University of Technology (TUT) Foundation of the Development of University of Gdansk (FRUG) Institute for Technical and Applied Physics (ITAP) German Maritime and Hydrographic Agency (BSH) University of Southern Denmark (SDU) Aarhus University (A) EQUIPMENT WildLife Acoustics SM2M 24 khz sampling frequency 16 bits resolution 2 16 =65536 Dynamics: 20log 10 (2 16 )=96 db

7 POSITIONS OF 38 ACOUSTIC DATA LOGGERS DEPLOYED IN THE BALTIC SEA km km max length 459 m max depth 40+ monitoring stations 12 months of continuous measurement 2 monitoring categories (A & B) Category A Monitoring - to establish information on the ambient noise in a location and to ground truth noise prediction, < 5 km? Category B Monitoring- to reduce uncertainty on source levels to be used as the input for modelling, close to shipping lane

8 POSITIONS OF 38 ACOUSTIC DATA LOGGERS DEPLOYED IN THE BALTIC SEA Recording minimum one channel Gain 0/20 db Hydrophone bandwidth min 10 Hz - 12 khz Sampling rate: min 24 khz Duty cycle: min 17 min per hour

9 POSITINS OF 5 ACOUSTIC DATA LOGGERS DEPLOYED IN THE POLISH PART OF THE BIAS PROJECT a) b) Puck Bay 2 Gulf of Gdansk 1 a) Map of the areas where buoys were deployed 1) Gulf of Gdańsk (71m) 2) Puck Bay (30m) 3) Łeba (16.4m) 4) Darłowo Ustka (40m) 5) Świnoujście (12 m) b) Number of ship routes at Puck Bay and Gulf of Gdansk

10 OUR BOAT ZELINT Hel Marine Station of the UG Institute of Oceanography

11 DEPLOYMENT Hel Marine Station of the UG Institute of Oceanography

12 RIG ON THE BOTTOM Hel Marine Station of the UG Institute of Oceanography

13 RECOVERY

14 RESULT OF THE TRAWL-NET STRIKE ON THE RIGS

15 ACOUSTIC DATA TIME COVERAGE QUALITY OF MEASUREMENTS Station Last deployment date Days since 1st of January till last deployment Days of recording Rec/Total , , The reasons of data gaps: Station 1 one of the SD cards was found thrown out of the socket, possible reason trawl net strike Station 2 battery failure

16 METRICS USED IN SOUND LEVEL ASSESSMENT MSP Mean Square Pressure RMS Root Mean Square SPL Sound Pressure Level SEL Sound Exposure Level PSL Peak Sound Level SNR Signal to Noise Ratio PSD Power Spectra Density PDF Probability Density Function SPL is the mean square pressure (MSP) relative to reference pressure pref MSP = p2 rms = 1 T න 0 T p t 2 dt SPL = 10log 10 p2 rms 2 [db re 1µPa] p ref p ref = 1 µpa

17 EXAMPLE OF SPL 63Hz AND SPL 125Hz VARIATION AT B1 WIND AND HEAVY SHIPPING TRAFFIC 1/3 octave bands Depth 71m-80 m SPL63,125 independed on wind speed AIS and VMS data

18 EXAMPLE OF SPL 2000Hz AND SPL 10Hz-10kHz VARIATION AT B1 WIND AND HEAVY SHIPPING TRAFFIC Depth 71m-80 m SPL2000,total depended on wind speed AIS and VMS data

19 MONTHLY AVERAGED NOISE SPECTRUM LEVEL, STATION B1, JANUARY DECEMBER 2014

20 AVERAGED NOISE SPECTRUM LEVEL, STATION B1, JANUARY 2014

21 MONTHLY AVERAGED NOISE SPECTRUM LEVEL, STATION B5, JANUARY DECEMBER 2014

22 AVERAGED NOISE SPECTRUM LEVEL, STATION B3, MARCH 2014

23 CUMULATIVE CURVES OF SPL FOR 63 Hz, 125 Hz AND 10 Hz 10 khz YEAR ships ships ships

24 SOUND PRESSURE LEVEL, 1/3 OCTAVE 125 Hz, FEB. NOV POLISH ECONOMICAL ZONE, DEPTH = 10 m

25 SOUND PRESSURE LEVEL, 1/3 OCTAVE 63 Hz, JANUARY 2014 BALTIC SEA Percentile 05% Layer 1 Depth 10 m

26 HOW TO CLASSIFY DIFFERENT SOURCES OF UNDERWATER AMBIENT NOISE? PARAMETRICAL ANALYSIS Example of 12 hours and 25 min continuous underwater ambient noise registration by recorder number 1 at 11 th of June ships

27 Example of 12 hours and 25 min continuous underwater ambient noise registration by recorder number 1 at 11 th of June Hz and 125Hz

28 PARAMETRICAL ANALYSIS OF AMBIENT NOISE SPECTRAL AND WAVELET PARAMETERS OF AMBIENT NOISE The normalized power spectrum of ambient noise is defined as: ~ S log A S 1 log A Smax 1 where A=10 5 const., S() - power spectral density function and its maximum value S max (). The classification parameters were defined as the relationships between parts of spectral density functions: Dq m 1 S f 1 1 f m 0 Ny S ~ ( f ) df where m=2, 4, 8, 16 and f Ny is the Nyquist frequency. S f m 1 S f 1 1 f m 0 Ny ~ Sdf

29 SPECTRAL AND WAVELET PARAMETERS OF AMBIENT NOISE The spectral moments of the r-th order: m r 0 ω r Sωdω spectral widths: spectral skewness: m m m m m m0m m 4 1 m ~ m~ channel dyadic decomposition (scale a = 2 j, j=1,..,7) 3 th -order Coiflet wavelet E j, Coif 3 b b max min C 2 a, b db where C(a,b) is the wavelet transformation coefficient, b min boundary values of scale b (time) and b max are

30 Clusters NOISE SOURCES CLASSIFICATION ALGORITHM Input vector of 23 spectral and wavelet parameters computed for each 1 second segment of signal Reduction of input data quantity Principal Component Analysis 3 uncorrelated Principal Components Decision about the number of clusters Calinski-Harabasz statistical criterion or (and) Subtractive Cluster Analysis Segmentation algorithm Unsupervised Kohonen s neural network or Fuzzy c-means cluster analysis

31 PRINCIPAL COMPONENTS Ship noise

32 Clusters Results of fuzzy logic clusterization algorithm bottom picture indication of ships Samples x 10 8

33 CONCLUSIONS We have knowledge about levels and sources of underwater ambient noise in the Baltic Sea. Goals of project were accomplished successfully. BIAS standards.

34 THANK YOU FOR YOUR ATTENTION