In addition to being one of the largest metropolises in the world, Istanbul is also one of the busiest routes of international maritime transport through the Strait of Istanbul (SoI). Currently, the average number of ships which passes through SoI is approximately 45,000. This number does not increase much due to the natural structure of SoI. So, larger ships with larger tonnages pass through SoI. In addition to the existing transit ship traffic in SoI, ship-based exhaust pollution caused by local maritime traffic adversely affects human health, comfort and the environment. In this study, the exhaust emissions generated by the ships of the local companies transporting in SoI were investigated. In addition, exhaust emissions from ships, one of the most important air pollutant emission sources such as motor vehicles, industrial processes and heating of residences in Istanbul, were calculated and presented. Ship exhaust gas emissions in SoI were calculated based on actual ship movements and ship machinery information using the bottom up method.

This paper represents the results of a case study of the interaction between sea level rise (SLR), subsidence and the consequences of this phenomenon on fresh water resources that may be subject to exacerbated salt-water intrusion. The possible reasons for rapid SLR at the permanent tide gauge station located on the southern coast of the Marmara Sea have been investigated based on time series data recorded since 1984. The population in the region relies mainly on groundwater resources for urban, tourism and agricultural water use, which represents a severe risk with regard to the replenishment of the coastal aquifer. Based on the findings, appropriate methods of assessing the coastal vulnerability to future SLR and recommendations for coastal zone management with emphasis on the protection of water resources are discussed.

The spatial and spectral information brought by the Very High Resolution (VHR) and multispectral satellite images present an advantage for Satellite-Derived Bathymetry (SDB), especially in shallow-water environments with dense wave patterns. This work focuses on Tavşan Island, located in the Sea of Marmara (SoM), and aims to evaluate the accuracy and reliability of two machine learning (ML) regression methods, Multi-Layer Perceptron (MLP) and Random Forest (RF), for bathymetry mapping using Worldview-2 (WV-2) imagery. In situ bathymetry measurements were collected to enhance model training and validation. Pre-processing techniques, including water pixel extraction, sun-glint correction, and median filtering, were applied for image enhancement. The MLP and RF regression models were then trained using a comprehensive dataset that included spectral bands from the satellite image and corresponding ground truth depth values. The accuracy of the models was assessed using metrics such as Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), and R² value. The RF regression model outperformed the MLP model, with a maximum R² value of 0.85, lowest MAE values from 0.65 to 1.86 m, and RMSE values from 0.93 to 2.41 m at depth intervals between 6 and 9 m. These findings highlight the effectiveness of ML regression methods, specifically the RF model, for SDB based on remotely sensed images in wave-dense shallow-water environments.

Flood risk, in the sense of damage that a ‘flood event’ can cause, was globally indexed and quantified to show the relationship between various natural and social factors. The Pressure and Release (PAR) model consisting of five key indexes; hazard, exposure, vulnerability, capacity soft countermeasures, and capacity hard countermeasures along with the entropy method was used to measure the uncertainty in information gathered. In this study, flood risk index was calculated for Yesilirmak Basin of Turkey based on PAR. The basin is one of the coastal watersheds of Turkey experiencing frequent flood events. Damage data covering years from 2000 to 2015 have been obtained from the Ministry of Agriculture and Forestry for Turkey. Candidate data for hazard, vulnerability, counter-measures and exposure were collected in accordance with temporal and spatial scales, and the collected data were compiled on a watershed basis. When grouping the sub-indicators, AWDO (Asian Water Development Outlook) was used and selected from a group of parameters covering a wide range of characteristics such as economy, health, vegetation, population, river according to their global presence, and data consistency. As long-term data accessibility was highly limited under basin conditions, data was obtained from relatively more reliable global sources such as United Nations’ databases on which data has been collected annually. The correlation among the collected parameter values was calculated based on the amount of damage that had occurred, and data with high correlation was included in the index account. Parameters that were not screened were rather weighted by using the entropy method and their effect on flood damage were determined. The calculated flood risk index based on PAR model was named as Yesilirmak Basin Flood Risk Index (Y-FRI). The resulting Y-FRI radar charts indicated that the most important variation occurred for the soft countermeasure index. In addition, the five main indices and the flood risk index was positively correlated with hazard, exposure and vulnerability indices, while negatively correlated with the countermeasures. This study showed that not only the hazard parameters directly contribute to flood formation, but also the vulnerability, exposure and countermeasure parameters that reflected the conditions of the region where the flood occurred have quantitatively influenced the flood damage.

Ocean Ambient Noise (OAN) results from both anthropogenic and natural sources. Varied noise sources are dominant in low (LFB: 10 to 500 Hz), medium (MFB: 500 Hz to 25 kHz) and high (HFB:>25 kHz) frequency bands. Mostly, LFB is dominated by anthropogenic sources. MFB that cannot spread over long ranges of sound sources contribute to the OAN. Ocean is an exceptionally noisy place. Ocean acidification (OAc) from rising Carbon dioxide (CO2 ) levels will result in decreased sound absorption and therefore, amplified levels of OAN. Carbon dioxide spewed into the atmosphere by burned fossil-fuel which dissolves in the seawater causes more acidic condition in oceans which has strong connection between chemical oceanography and sound propagation. As the ocean becomes more acidic, sound absorption at LFB decreases and acidic oceans would result in significant decreases in ocean sound absorption. In the recent years, the acoustic environment of oceans has reacted to transformations in both natural and anthropogenic impacts. Greenhouse gases concentrations, especially CO2 , rises in atmosphere due to industrial revolution. CO2 dissolved in the seawaters deposited in two major forms (carbonate and bicarbonate), which both lead to decrease pH of surface waters. Over the last 400 million years, pH of oceans has been stable around 8.2 globally. Latest investigations suggest that global pH is around 8.1 globally and various general oceanic circulation models (GOCM) calculate that, emissions could reduce ocean pH by a degree between 0.4 units (according to moderate approach) and 0.7 units (according to an aggressive one) by the end of this century. This article discusses the CO2 considerations both in the atmosphere and hydrosphere which are directly related with seawater pH and oceans noise levels.

Underwater noise pollution (UNP) has become a major concern in marine habitats, which is intense anthropogenic noise in the marine (aquatic) environment. It is caused by ship traffic, oceanographic experiments, and use of explosives in geophysical research, underwater construction, active sonars and seismic survey techniques. Oceans are much nosier than 1960s. Narrow and shallow channel noisy aquatic environments where noise levels reach the highest value is not surprising. The Strait of Istanbul (SoI; Bosphorus) is one of the most important maritime passages (app. 50 000 vessel/year or 140 vessel/day) which is situated between the Black Sea and the Aegean Sea are also biologically extremely important gateway not only it provides access to a channel. Many of the varieties of fish migration hunting value are realized through the TSS. Local maritime traffic is another important acoustic sources which are more than 3 000 elements (Kesgin and Vardar, 2001) of everyday local traffic in SoI, which are causing noise in the 2 and 10 kHz range. Large vessels create signals both in bands below 1 kHz (main engine, electrical instruments) cavitation noise creates higher frequency bands. Almost all elements of marine traffic in SoI located therefore encountered UND in all bands.