A Meta-Analysis to Understand the Variability in Reported Source Levels of Noise Radiated by Ships From Opportunistic Studies

Abstract

Background: Commercial shipping is identified as a major source of anthropogenic underwater noise in several ecologically sensitive areas. Any development project likely to increase marine traffic can thus be required to assess environmental impacts of underwater noise. Therefore, project holders are increasingly engaging in underwater noise modeling relying on ships' underwater noise source levels published in the literature. However, a lack of apparent consensus emerges from the scientific literature as discrepancies up to 30 dB are reported for ships' broadband source levels belonging to the same vessel class and operating under similar conditions. We present a statistical meta-analysis of individual ships' broadband source levels available in the literature so far to identify which factors likely explain these discrepancies. Methods: We collated ships' source levels from the published literature to construct our dataset. A Generalized Linear Mixed Model was applied to the dataset to statistically assess the contribution of intrinsic (i.e., related to ships' static and dynamic attributes) and extrinsic factors (i.e., related to both the protocol for hydroacoustic data acquisition and the noise data reduction procedure) to the reported broadband source levels. Results: Amongst intrinsic factors, ships' speed-over-ground 15.39 dB × log 10 v 1 knot , p-value < 0.001 , ships' width 12.03 dB × log 10 b 1 m ; p-value < 0.001 , and ships' class (−6.07 to 2.08 dB; p-value ∈ [< 0.001 to 0.036]) have shown the strongest correlations with broadband source levels. The hydrophone-to-source closest point of approach-4.83 dB × CPA 1 nmi ; p-value < 0.001 and the correction for surface-image reflections (21.73 dB; p-value = 0.002) contribute the most to explain the reported ships' broadband source levels' variability amongst extrinsic factors. Conclusions: Our meta-analysis confirms a consensus that speed regulation can effectively reduce instantaneous ships' source levels. Neglecting Lloyd's mirror effects through the abuse of non-corrected spreading laws for propagation loss directly leads to a generalized underestimation of the ships' source levels retrieved from the literature. This could eventually be addressed by a wider adoption of standardized methods of hydrophone-based sound recordings and of data processing to homogenize results and facilitate their interpretation to conduct environmental impact assessment.

Underwater noise mitigation in the Santa Barbara Channel through incentive-based vessel speed reduction

“…Our results show a positive relationship between SOG and SL, similar to past studies 4 , 6 , 8 , 22 , 23 , 25 . Across all vessels, our relationship of 2.0 dB s m −1 (1.0 dB/knot) is within 0.2 dB s m −1 from the relationship found in Viers et al (2016) and within 0.1 dB/knot from the relationship found in ships greater than 250 m in length in Simard et al (2016) 22 , 23 .…”

Modelling of underwater noise emissions by ships in Klaipėda Strait, Lithuania

“…Still, other analogue source spectral models, i.e. SONIC, as well as a model by Wittekind (2014) or others, might be utilized (see also Chion et al 2019). However, some ship source models require special data input (Liefvendahl et al 2015).…”

“…Still, the absolute error can be slightly greater in higher and lower frequency bands. It should also be noted that studies of the comparison of available empirical ship source models reveal an impact of experimental methodology on the modelling results, using these models where the factors, such as the closest point of approach (CPA), compensation of the surface image effects (Lloyd mirror effects), hydrophone directionality angle, and estimation of ships source depths, may affect measurement results which were used to fit empirical models (Chion et al 2019).…”

Sounding the Call for a Global Library of Underwater Biological Sounds

“…Anthropogenic noise, such as that from vessels, exploration, construction, and aerial vehicles (Reine et al, 2014;Newhall et al, 2016;Pangerc et al, 2016;Erbe et al, 2018;Chion et al, 2019;McCauley et al, 2021;Parsons et al, 2021), is a growing pollutant in the underwater environment and high ambient noise levels, such as those found in areas of intense human activity, inhibit signal detection (Hildebrand, 2009;Duarte et al, 2021). If the observer knows a target species' signal characteristics, these sounds may be more easily detected, but without prior knowledge of either presence or structure of sounds, listening through the noise can be difficult.…”