Erratum to "Automated identification and assessment of environmental noise sources" [Heliyon Volume 9, Issue 1, JANUARY 2023, Article e12846].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e12846.].

Automated identification and assessment of environmental noise sources.

Noise pollution is one of the major health risks in urban life. The approach to measurement and identification of noise sources needs to be improved and enhanced to reduce high costs. Long measurement times and the need for expensive equipment and trained personnel must be automated. Simplifying the identification of main noise sources and excluding residual and background noise allows more effective measures. By spatially filtering the acoustic scene and combining unsupervised learning with psychoacoustic features, this paper presents a prototype system capable of automated calculation of the contribution of individual noise sources to the total noise level. Pilot measurements were performed at three different locations in the city of Ljubljana, Slovenia. Equivalent sound pressure levels obtained with the device were compared to the results obtained by manually marking individual parts of each of the three measurements. The proposed approach correctly identified the main noise sources in the vicinity of the measurement points.

Impact of COVID-19 on environmental noise emitted from the port.

Identification of noise sources and their ranking is a crucial part of any noise abatement program. This is a particularly difficult task when a complex source, such as a seaport, is considered. COVID-19 epidemic has had a significant impact on environmental noise related to road, rail, air and ship traffic and provided a unique opportunity to observe immediate noise reduction. In order to identify the noise sources, whose reduction was most effective in reducing noise from the port area, this study compared and quantified noise emissions between the historical and epidemic periods. Environmental noise measurements from three noise monitoring stations at the port boundary were analysed. In addition, noise emissions from ship, road, rail and industry as well as meteorological data in the historical pre - COVID-19 (January 2018-February 2020) and COVID-19 (April 2020) period were analysed in detail. The characteristics of the noise sources mentioned, geographical data and noise measurements were used to develop and validate a noise model of the port area, which was used to calculate noise contour maps. Our results show that the reduction in noise levels observed at all monitoring stations coincides with the reduced shipping traffic. The A weighted equivalent sound pressure levels in the day, evening and night periods were reduced by 2.2 dB to 5.7 dB compared to the long-term averages, and the area of the 55 dB day-evening-night noise contour was reduced by 23%. Compared to the historical period, the number of people exposed to noise levels above 55 dB(A) in the day-evening-night period due to shipping and industrial activities was reduced by 20% in the COVID-19 period. Such results show that environmental noise generated by moored ships is a problem for port cities that should be regulated internationally. In addition, this paper provides precise guidance on noise emission characteristics, ship categorisation and the post-processing of long-term measurement data, taking into account wind conditions and undesired sound events, which can be applied to future research at other locations near shipping ports and used to prepare strategies for noise reduction in ports.