Variability in airborne noise emissions of container ships approaching ports

Abstract

Container ships emit airborne noise that can vary significantly due to diverse vessel characteristics and operating conditions. This study aims to investigate the factors influencing noise emission from container ships moving in ports to enhance our understanding and improve noise prediction models. Using a dataset comprising long-term sound pressure level measurements, video recordings, and weather station data, a multiple regression analysis was conducted to assess the impact of static (ship-specific) and dynamic (pass-by specific) variables on noise emission. Static variables included ship dimensions and age, while dynamic variables encompassed distance from the microphone, speed, and draught. A k-means unsupervised clustering analysis was performed using 1/3rd octave band spectra to identify subcategories of container carriers based on sound emission characteristics. Significant correlations between emissions and both static and dynamic variables were found. The k-means clustering analysis yielded distinct subcategories of container carriers based on their sound emission profiles. This study highlights the importance of considering various factors, including ship characteristics and operating conditions, when assessing noise emission from container ships. By better understanding the factors contributing to noise emission, we can effectively mitigate noise pollution and minimize the impact on surrounding communities.