D2.1 Report on Environmental Risk Assessment North Adriatic Maritime Incident Response System National Institute of Oceanography University of Ljubljana Faculty of Maritime and Applied Geophysics – OGS (IT) Studies and Transport Portorož (SI) Adriatic Training and Research Centre for Administration of the Republic of Slovenia Accidental Marine Pollution Preparedness for Civil Protection and Disaster Relief (SI) and Response – ATRAC (HR) D2.1 Report on Environmental Risk Assessment Report on Environmental Risk Assessment D.2.1 Authors: Vinko Bandelj, Fabrizio Gianni, Donata Canu, Célia Laurent, Stefano Querin, Serena Zunino (OGS), Valter Suban, Urban Pegan, Marko Perkovič, Blaž Luin (UL-FPP), Vedran Martinić, Anja Pilepić (ATRAC) Publisher: Central European Initiative (CEI), University of Ljubljana, Faculty of Maritime Studies and Transport (UL-FPP) Copyright: Central European Initiative (CEI) Year of publication: 2024 Print run: printed digital y Co-funded by the European Union North Adriatic Maritime Incident Response System Risk Assessment finalised on February 28th, 2023. 4 5 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 1 / Introduction This report describes the activities carried out in the framework of the Activity 2.1 – Environmental Risk Assessment of NAMIRS. According to the project proposal, the goals and scopes of the Activity were to perform an Environmental Risk Assessment. The report 1 showcases a methodology to conduct the oil spill risk assessment by integrating different tools such as the identification of particularly sensitive areas and the assessment of their vulnerability, the statistical and expert-based analysis of the ship traffics and oil spill probability, and a numerical oil spill model statistically assessing the oil spill hazard. The vulnerability assessment was done through stakeholder involvement following a participatory approach, as detailed in the report in order to assign scores (weights) to the different receptors. Through the assessment model developed, environmental damages in case of ship accidents (collision/sinking/grounding) are studied, taking especially into account the impact of oil-spill in marine sensitive areas and their secondary but relevant impact on the economy, and on the life of people living on the shore. The results of Activity 2.1. will help in contingency planning which is the main outcome expected from Introduction NAMIRS. The activity was led by OGS, coordinated by URSZR in the wider context of NAMIRS WP2, and saw the active collaboration of the University of Ljubljana (UL-FPP) for most activities, and of ATRAC for the organization of the stakeholders’ workshop in Croatia. Figure 1: Schematic representation of Activity 2.1, its division in 4 tasks, and the related specific subtasks. The logos of the partners indicate the main responsibility for each Task or subtask, but all three partners actively collaborated during the whole Activity 2.1. 6 1 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment For operative purposes, activity 2.1 was subdivided into four tasks (Fig. 1): 1. Risk of Accidents 2. Oil Spill Simulations 3. Vulnerability of coastal and Sea Areas in the Northern Adriatic Sea 4. Risk Assessment The four tasks are devoted to the definition of the hazard (Task 1), exposition (Task 2), and vulnerability (Task 3) of the area of interest, while the risk assessment is computed in Task 4 is the risk, as a function of hazard, exposition, and vulnerability, is computed. The organization of the report is as follows: Section 2 is devoted to the description of the activities of UL-FPP for the analysis of the risk of accidents in the Northern Adriatic Sea (Task 1); Section 3 to the description of the activities of OGS regarding oil spill simulations (Task 2); Section 4 to the description of the activities regarding the stakeholders involvement, i.e. to the preparation of the questionnaires and to the activities regarding the stakeholders’ workshops on behalf of UL-FPP, ATRAC and OGS; Section 5 to the vulnerability mapping and assessing in the coastal areas of the Northern Adriatic Sea; Section 6 to the description of the activities for the computation of the final risk index and the production of the relative mappings; Section 7 brings some general final remarks and suggestions for possible future developments of the work. 2 3 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 2 / Traffic data analysis and hazard estimation The Faculty of Maritime Studies of the University of Ljubljana, as a partner in the NAMIRS 2 project, was commissioned to study maritime traffic in the northern Adriatic from the point of view of the risk of maritime accidents resulting from the significant oil release (Task 1 of Activity 2.1). 2.1 Introduction The aim of the Section is to examine the nautical risks, focusing on potential accidents occurring during the vessel en-route to and from one of the major ports in the northern Adriatic Sea. The risk assessment is carried out for actual traffic conditions and comparative simulations, including traffic separation systems. The focus of the risk assessment Traffic data is on commercial vessels, but fishing and recreational vessels are also considered to some extent. Marine casualty risk assessment is one of the bases for implementing measures to reduce the number of marine casualties, but also one of the bases for improving the overall risk management of such casualties. This includes the analysis and design of the analysis and response measures available in the region, which measures and how they should be improved in order to achieve a balanced and satisfactory response to disasters, thus reducing the consequences in terms of human lives, environmental pollution and economic damage. Neither Italian, Slovenian nor Croatian legislation do prescribe approaches or methods hazard estimation for assessing the risk of accidents at sea. Based on the international scientific and technical literature, the recommendations of the International Maritime Organization (IMO) and of the European Maritime Safety Agency (EMSA), and the Emergency Response Centre for Marine Pollution in the Mediterranean (REMPEC) combination of qualitative and quantitative approaches were applied. This is particularly important in order to obtain the most realistic assessment possible, given the local situation, as in some parts of the risk assessment it is practically impossible to give a quantitative assessment based on the local situation, simply because there are no statistics or relevant events (e.g. the assessment of the probability of a major spill of hazardous substances on the water cannot be based on the frequency of events in our area, as this has not yet happened). The collision and stranding frequency calculations are based on historical event statistics and near misses. These are based on vessel movement data via AIS. The consequences of accidents are described in qualitative terms. The identification of risk sources includes the screening of all hazardous substances on board ships in transit that could be released into the environment and thus cause adverse effects. 4 5 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 2.2 AIS data Near offshore traffic and coastal traffic along with confined waters traffic can be easily monitored using shore AIS base stations. The system, called AIS (Automatic Identification System), is primarily for ships. It allows a vessel to detect another vessel in time, even in poor visibility when radar is unable to indicate all hazards in the water, and to obtain the necessary information about the sighted vessel without establishing a radio link. Maritime industry stakeholders built the AIS with the goal of improving maritime safety, security, and their assessment. The AIS was launched as a joint effort of the International Maritime organization (IMO), the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA), the International Telecommunication Union (ITU), and the International Electrotechnical Commission (IEC). Originally, AIS was introduced on certain types of International Convention for Safety of Life at Sea (SOLAS) ships to assist the Officer of the Watch (OOW) in making decisions in the event of a collision. Naturally, the AIS system was immediately used in Vessel Traffic Service (VTS) systems for vessel traffic control. Officially, AIS was first recognized in 1998 by a resolution of the International Maritime Organization (IMO) adopted at the 69th session of the Maritime Safety Table 1: Transmission period of dynamic data (IALA, 2016). Committee (MSC). The AIS device is capable of transmitting information to vessels and coastal authorities automatically, with the required accuracy and using only the desig-The AIS system must be capable of processing at least 2000 messages per minute when nated radio frequencies. AIS operates in the VHF frequency band. It has been assigned used as a ship reporting system. The technical characteristics of AIS, such as variable two channels: AIS 1 (161.975 MHz - marine band channel 87B) and AIS 2 (162.025 MHz transmission power, operating frequencies, modulation and antenna systems, are spec- - marine band channel 88B). The nominal reporting intervals for data transmission vary ified in the ITU (International Telecommunication Union) standards. The envisaged ca-from 2 seconds to 6 minutes and depends on the type of AIS station, the group of mes-pacity of 4500 telegrams per minute is assumed to be sufficient for unrestricted ship-to-sages, the navigational status, the speed and the course change of ships (Burmeister ship (2S) and even ship-to-shore (4S) use, with a typical range of 20 nautical miles (NM) et al., 2014). Slower ships send kinematic data every 10 seconds, medium speed ships between ships and up to 40 NM between ship and shore. The theoretical range of the every 6 seconds, high speed ships every two seconds. If the ship changes heading, the system is given by the following equation: transmission intensity increases by a factor of 3 (for slower and medium speed ships). Table 1 shows the transmission intensity of static and dynamic information for Class A dmin=2.5(√( heightTXant[ m] )+√( heightRX ant [ m] ))= range [ Nm] and B and for Single and Dual Channel Transceivers. AIS Transponders can receive all transmission information from both AIS channels simultaneously and combine the in-The main AIS receiving antenna in Slovenia is located on Slavnik, while in Croatia (for the formation from both channels into a single data stream. The standards of transmission, northern Adriatic Sea) it is located on Učka. Both antennas are on excellent locations, so types, the format of messages and symbols, make it simple for users to identify, monitor, there is also good monitoring of shipping traffic. For example, a VHF antenna at 1030 and track targets detected by AIS. metres receiving data from a large merchant vessel with a VHF antenna at 49 metres has a range of 100 NM: 2.5(√1026+√49) ≈ 100 Nm Today’s applications of AIS data have shifted from use for collision avoidance, identification, and tracking to monitoring shipping routes, maritime traffic trends, risk analysis, marine accident investigations, near-miss investigations, search and rescue operations, waterway planning, management and maintenance using AtoNs (Aid to Navigation), traffic simulation, and forecasting, fisheries monitoring, environmental monitoring, prevention of illegal activities at sea. 6 7 D2.1 / Report on Environmental Risk Assessment Fig. 2 shows the current ship status in the Northern Adriatic, while Fig. 3 shows yearly The Slovenian Maritime Administration integrates AIS from the following sources: based traffic density. This information is available from the MarinTraffic provider, which 1. AIS BS SLAVNIK - MMSI 002780201 collects AIS data on a voluntary basis. Latitude= 45°32.028947’ --> 45.53381578° Longitude= 13°58.517946’ -->13.97529910° MSL= 1025.68m 2. AIS BS IZOLA - MMSI 002780202 Latitude= 45°32.669470’ Longitude= 13°41.204462’ MSL= 135.88m MSLGPS = 135.88+44.477=180.357m 3. AIS BS KOPER - MMSI 002780203 (MSL=54.387 m) 4. AIS BS POORTOROŽ - MMSI 002780204 (Installed by UL FPP and shared with the MarineTraffic system) 5. MARES stream - display of AIS data extracted from the national AIS systems of the Mediterranean countries. The main service provided by the AIS server is the collection and transmission of AIS data in real time and its storage in databases. The AIS storage is done with the commercial software of Transas/Wartsila “TranDB”, which writes the data into a MS SQL database, where due to the amount of data the messages are recorded in a separate table for each message type and each day: Figure 2: Example of the vessel position on 07.09.2022 at 08:25 in the study area. (Source: MarineTraffic) Table 3: explanations of Table 2: AIS messages grouped. the abbreviations in Table 2. Name of the table YYYY Year dbo.part_(YYYY-MM-DD hh:mm)_MsgPos MM Month dbo.part_(YYYY-MM-DD hh:mm)_MsgPos2 DD Day dbo.part_(YYYY-MM-DD hh:mm)_Msg5 Hh Clock dbo.part_(YYYY-MM-DD hh:mm)_MsgOther1 mm Minute dbo.part_(YYYY-MM-DD hh:mm)_MsgOther2 msg Message dbo.part_(YYYY-MM-DD hh:mm)_MsgNonAIS Other Other messages Up to 250 million AIS messages are recorded monthly only for the Northern Adriatic area. Commercial devices do not allow the export of large amounts of data from AIS without disrupting the ongoing AIS recording. Therefore, we set up a parallel offline server on the UL-FPP with identical (commercial) software and synchronized the databases with those available on the UL-FPP. To export data from a specific area and time period, we used a Python 3.6 script that uses the pymssql library and writes the desired data to CSV files for further processing. When exporting, the geographic coordinates must also be recal-Figure 3: All ships traffic density in 2020-2021 in the study area. (Source: MarineTraffic) culated since they are not in the standard notation. 9 D2.1 / Report on Environmental Risk Assessment The data is exported to a separate file for each month, as this makes the most sense 2.3 Traffic data – spatial distribution analysis given the number of records. For further processing, we used the Pandas tool for Python to import the data. This al-The shipping densities are discussed in more detail below. First, ships are classified into lows to quickly filter the data by various criteria once it is loaded into memory. To process size classes, small ships less than 50 meters in length, which includes all fishing boats, the large amount of AIS data, we developed three batch processing programs. During pleasure crafts and service vessels, then up to 150 meters, which includes mainly coast-the pre-processing, only data limited to the desired geographical area is extracted, the er ships, ships up to 230 meters, and ships up to 300 meters in length, and ships larger original SQL database record is exported as a csv file for a time period of two months. than 300 meters, which includes mainly container ships. For all these size classes, den-The files were read using the Pandas library for Python. As the ship trajectories are given sity maps are then produced for the distribution of average ship size, number of ships, at different sampling rates, depending on the ship speed and navigation status (Table occupancy time of ships and speed distribution. 1), all trajectories were resampled to a period of 10 s. This simplified or speeded up the Fig. 5 showing the distribution of average ship lengths within the 50-meter class clearly calculation steps. identifies the positions of the larger service ships close to 50 meters in length i.e., the red When no information about a ship was available for more than 120 s, it was assumed lines connecting the offshore platforms. Fig. 6 shows the distribution of ships (number to be stationary. The resampling was performed by linear interpolation of velocities and of ships in each cell) in a class within 50 meters of the ship’s length. The highest density positions between known points. of small vessels is in the coastal zone on the Croatian side, while Italian fishing boats sail all the way to the border zone. Fig. 7 shows the distribution of occupancy time (in sec-In addition to the pre-processing of the input data, a grid was defined for the calculation onds) in each cell, for ships less than 50 meters in length. This figure accurately depicts by setting the longitude and latitude step. the locations of fishing boats, especially the stationary locations along the Istrian coast. Fig. 4 shows vessel density due to all traffic in 2019 in the northern part of the Northern In addition to the obstacles in the way shown in the previous figures, shipping speeds are Adriatic Sea. The largest number of messages (maximum 13,105,332 messages) is in also important. Fig. 8 shows the distribution of these, which are highest on service vessel the ports and terminals marked in purple (e.g., the Rovigo LNG terminal). courses, up to 15 knots on average. Figure 4: High resolution traffic density chart in 2019, main routes, ports are terminals are clearly identified. Figure 5: Average vessel size distribution (L<50m) 11 D2.1 / Report on Environmental Risk Assessment Figure 6: Ships position distribution (L<50m). Figure 8: Ship velocity distribution (L<50m) In the class of ships up to 150 meters in length, the distribution of ship average length is almost equally spread over the whole area except for the part dedicated to the offshore industry (Fig. 9). The figure gives a slight indication of the main traffic flows. The distribution of vessels (number of vessels in each cell) in a class within 50 and 150 meters of length is shown Fig. 10. The highest density of coastal vessels is in the main lanes leading to major regional ports. At first glance, the temporal distribution for this class of vessels (Fig. 11) looks like the positional distribution, but a closer look clearly shows anchorages that could obstruct ships as they navigate or be a potential location for collisions between ships. The distribution of ship speeds also evidences the anchorages (Fig. 12), but also the courses of the larger offshore supply vessels. Furthermore, the figure shows that smaller merchant ships move on average steam at less than 15 knots. As in the previous length class, the distribution of the average length of vessels in the 150 to 230 meter class is not evenly spread over the whole area. Fig. 13 gives a better indication of the main traffic flows. Figure 7: Temporal distribution (L<50m). 13 D2.1 / Report on Environmental Risk Assessment Figure 9: Average vessel size distribution (50L<150m). 15 D2.1 / Report on Environmental Risk Assessment Figure 14: Ships position distribution (150300m). Figure 20: Ship velocity distribution (230300m). 21 D2.1 / Report on Environmental Risk Assessment Figs. 25-26 shows the distribution of traffic as well as the density of ships, divided in four main categories: tankers, service vessels (well evident is a grid pattern of the trajectories of service vessels exploring for hydrocarbons under the seabed in Croatia), passenger ships (ferries and cruisers), and finally ro-ro ships, dry cargo ships such as container ships, general cargo ships, and bulk carriers. It can be seen that the vast majority of ships sail along the entire Adriatic Sea, irrespective of the type of ship. Figure 23: Temporal distribution (L>300m). Figure 25: Traffic density for Tankers and Service vessels. Figure 24: Ship velocity distribution (L>300m). Figure 26: Traffic density for Cruisers and dry cargo ships. 23 D2.1 / Report on Environmental Risk Assessment 2.4 Incidents and accidents – some examples Fig.29 shows a close encounter between the tanker “Seanostrum” and a ship improperly intersecting the TSS “Niyazi Gokalp”. The traffic survey shows that shipping in the region is moderate, that there is a wide variety of ships, and that although there is a traffic separation scheme, accidents can happen. Figs. 27-30 provide several examples of possible incidents in the area. Fig. 27 shows a realistic example of a possible collision within a Traffic Separation Scheme (TSS). A ship sailing inside the TSS, “Trident Hope”, assumed it had the right of way, and 6 minutes before the collision it started an avoiding maneuver. Figure 29: Close encounter of the “Niyazi Gokalp” with the tanker “Seanostrum”. The last example (Fig. 30) shows the large merchant ship “Und Atilm” (length 195 m - sister ship to the recently burnt “Und Adriyatik”, it is noted that these ships generally violate Figure 27: Close encounter at the exit of the TSS - “Trident Hope”. traffic regulations and that they also discharge oily water into the sea - proof of which Fig. 28 shows a near miss when the ship “Anamcara” intersected the TSS incorrectly and will follow below in the section on illegal discharges) passing through the anchorage at at the same time made two wrong turns inside the TSS. a high speed of 20.2 knots. Figure 28: Near collision - “Anamcara”. Figure 30: High speed crossing of an anchorage - “Und Atilm”. 25 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 2.5 Ship collision risk assessment A simplified statistical model has been used to model the probability of a collision at sea. The model ignores a number of elements and their correlations/dependencies as Risk is assessed by first assigning a value to the probability of the event occurring and it is simply based on statistical data obtained from observations of real shipping traffic. then to the severity of the consequences for shipping. Typically, two values are multiplied The biggest unknown parameter in this type of modelling is the exact number of close to form the risk matrix. Finally, the result is assigned to the risk matrix and classified as encounters. Some of them can be selected from the AIS archive, but it should be noted low, moderate, or high. The risk rating indicates the magnitude and acceptability of the that these encounters are more numerous because the area off Koper is a crossing of risk and determines whether the task can be performed and when additional control mea-shipping lanes, where there are often fishing boats and other vessels that are not includ-sures are required to reduce the risk to ALARP (As Low As Reasonably Possible). The ed in the traffic archive. Therefore, the only way to determine the parameters for close probability of collision and grounding for the Gulf of Trieste was first calculated in 2009 encounters in such complex traffic regimes is to model traffic flows over longer periods using a stochastic approach developed by Gucma et al. (2006), at a time when IWRAP of time. A simplification of the calculation is given by equating the collision probability was not available. The calculated unplanned event was significantly higher, mainly due to over the whole area, which is also consistent with the available incident. The calculated the 30% higher traffic volume, which was not as organized as it is now. Both approaches collision probabilities for individual encounter states (head-on, crossing, and overtaking) are presented here. A stochastic simulation model was used for the safety analysis of are higher than 1*10-5, which is also the usual size class for the application of this type complex maritime traffic (Gucma et al., 2016). The modular structure of the model is of safety analysis at sea. In the following, 30 typical waterways in the Gulf of Trieste are shown in the figure below. This type of model can be used to analyse maritime traffic selected for input into the model, as shown in the following figures. First, the complexity in different aspects: collision, grounding, collision with fixed objects, indirect collisions of the traffic flows and trajectories is presented. such as anchoring, and wave generation causing damage to the shore. The model can be extended and complemented with sub-models according to the research objectives. This modelling methodology is well established and has been applied in several case studies. Figure 31: Diagram of the stochastic model for navigation safety assessment. Figure 32: Selected fairways for modelling potential accidents in the Gulf of Trieste. 26 27 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The traffic simulation is carried out in batches of 5 years. The increase in traffic over this period and the impact on safety can be analyzed relatively well. The results of the locations of potential incidents are presented in the next Figs. 33-34. The processed data show that vessels do not only sail in the regulatory areas, but also in the separation zones, in the lanes in the opposite direction, and in the local coastal traffic areas. Despite the separation scheme, inappropriate and dangerous maneuvers are sometimes made by vessels passing through the area (Fig. 33). However, this does not mean that it is always ships that violate the rules, as their maneuvers may be the result of violations by other smaller vessels (mainly boats) that do not transmit AIS and are not detected by radars. The model showed that the highest density of collisions in the area is located between the separation lines - the so-called ‘precautionary zone’ (Fig. 34). The probability of a major accident occurring during this period is estimated to be once every 120 years. The simulation showed that the time between collisions with just a 30% increase in traffic, an accident can be expected every 80 years. Table 4 gives the detailed positions with encounter types and expected spillage depend-Figure 34: Simulation of possible collision locations. ing on speed, ship size, and ship loading condition for the Gulf of Trieste. Figure 33: Analysis of the data relative to vessels sailing in the Gulf of Trieste. 28 29 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Table 4: Potential accident locations in the Gulf of Trieste modelled stochastically, with indication of the 13.61960735° E 45.61869464° N Crossing 1500 30000 15000 type of accident, the type of oil product involved, and the estimated quantities of products released in the water. 13.60886889° E 45.62023407° N Crossing 1500 30000 15000 13.62366451° E 45.62615882° N Crossing 1500 30000 15000 Type of Bunker Oil Crude Product Longitude Latitude accident (HFO_&_LSFO) Oil (diesel) 13.63806024° E 45.62619455° N Crossing 1500 30000 15000 13.69392222° E 45.55979235° N Allision 1000 5000 13.60155729° E 45.62660554° N Crossing 1500 30000 15000 13.70292459° E 45.56518537° N Allision 1000 5000 13.63005648° E 45.63064472° N Crossing 1500 30000 15000 13.70252715° E 45.57119571° N Allision 1000 5000 13.61270392° E 45.63237405° N Crossing 1500 30000 15000 13.67819703° E 45.57220059° N Allision 1000 5000 13.58491862° E 45.63429868° N Crossing 1500 30000 15000 13.67304983° E 45.57920814° N Allision 1000 5000 13.58887546° E 45.63645763° N Crossing 1500 30000 15000 13.64519026° E 45.58184783° N Allision 1000 5000 13.59034623° E 45.63867045° N Crossing 1500 30000 15000 13.68556035° E 45.62439027° N Allision 1000 15000 5000 13.58274019° E 45.64074606° N Crossing 1500 30000 15000 13.65690631° E 45.63141912° N Allision 1000 15000 5000 13.62706152° E 45.64607300° N Crossing 1500 30000 15000 13.69676223° E 45.63165696° N Allision 1000 15000 5000 13.62623297° E 45.64818178° N Crossing 1500 30000 15000 13.69676223° E 45.63165696° N Allision 1000 15000 5000 13.62217252° E 45.65244776° N Crossing 1500 30000 15000 13.67110737° E 45.64130274° N Allision 1000 15000 5000 13.61213715° E 45.73400742° N Crossing 1500 30000 15000 13.65506813° E 45.64424405° N Allision 1000 15000 5000 13.50790922° E 45.55830098° N Overtaking 500 5000 5000 13.69695240° E 45.64918949° N Allision 1000 15000 5000 13.49845857° E 45.55875748° N Overtaking 500 5000 5000 13.69822560° E 45.65170179° N Allision 1000 15000 5000 13.51051232° E 45.55931577° N Overtaking 500 5000 5000 13.65092488° E 45.65531408° N Allision 1000 15000 5000 13.53190853° E 45.57147604° N Overtaking 500 5000 5000 13.65795693° E 45.65778071° N Allision 1000 15000 5000 13.44308846° E 45.58248147° N Overtaking 500 5000 5000 13.67599200° E 45.65971947° N Allision 1000 15000 5000 13.53044396° E 45.58270515° N Overtaking 500 5000 5000 13.65295331° E 45.66089701° N Allision 1000 15000 5000 13.54964278° E 45.58444207° N Overtaking 500 5000 5000 13.63890604° E 45.59745103° N Crossing 1500 30000 15000 13.55830186° E 45.58758087° N Overtaking 500 5000 5000 13.63855922° E 45.60017275° N Crossing 1500 30000 15000 13.55833543° E 45.58795816° N Overtaking 500 5000 5000 13.61030592° E 45.60711050° N Crossing 1500 30000 15000 13.45125168° E 45.60558592° N Overtaking 500 5000 5000 13.60945121° E 45.61050520° N Crossing 1500 30000 15000 13.50567977° E 45.61209393° N Overtaking 500 5000 5000 13.58976625° E 45.61298687° N Crossing 1500 30000 15000 13.56222224° E 45.63088427° N Overtaking 500 5000 5000 13.61615616° E 45.61684767° N Crossing 1500 30000 15000 13.63880156° E 45.66263050° N Overtaking 500 5000 5000 13.63630284° E 45.61790272° N Crossing 1500 30000 15000 13.62449205° E 45.61830636° N Crossing 1500 30000 15000 30 31 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The numerical results were then expertly processed and extrapolated from the Gulf of Table 5: Potential accident locations in the North Adriatic Sea - an extrapolated qualitative approach, Trieste to the entire North Adriatic region. Fig. 35 shows the locations of potential en-with indication of the type of accident, the type of oil product involved, and the estimated quantities of products released in the water. counters in the Northern Adriatic Sea, while Table 5 gives more detailed positions with encounter types and expected spillage depending on speed, ship size and ship loading condition. The most dangerous areas for incidents are anchorages and locations where Type of Bunker Oil Crude Product Longitude Latitude waterways intersect. accident (HFO_&_LSFO) Oil (diesel) 14.48815016° E 43.64815717° N Crossing 1500 15000 15000 14.11154590° E 43.68319013° N Crossing 1500 15000 15000 13.54751533° E 43.69720331° N Allision 1000 5000 13.66837902° E 43.73573956° N Crossing 1500 15000 13.69465374° E 43.94243399° N Crossing 1500 15000 14.25868431° E 43.97571530° N Overtaking 500 5000 5000 13.74720317° E 44.00199001° N Crossing 1500 15000 13.78048448° E 44.06154604° N Allision 1000 5000 13.04128914° E 44.10533723° N Allision 1000 10000 5000 13.20944732° E 44.18591302° N Crossing 1000 5000 13.06931550° E 44.22269763° N Allision 1500 15000 14.02921846° E 44.22620092° N Overtaking 500 5000 5000 13.14288471° E 44.31553496° N Crossing 1000 5000 13.79800096° E 44.35407121° N Overtaking 500 13.77172624° E 44.50296126° N Crossing 1500 30000 15000 13.65962079° E 44.50296126° N Crossing 1500 15000 12.98873971° E 44.67111944° N Overtaking 500 13.41088681° E 44.77271501° N Crossing 1500 15000 13.50372414° E 44.80424467° N Crossing 1500 30000 15000 Figure 35: Probable accident locations in the Northern Adriatic Sea. 12.93969357° E 44.90233694° N Overtaking 500 5000 5000 13.31980113° E 45.00743581° N Crossing 1500 15000 15000 13.12711988° E 45.05473030° N Crossing 1500 12.70146948° E 45.08450831° N Crossing 1500 14.23941618° E 45.14231268° N Crossing 1500 15000 15000 12.51754647° E 45.18435223° N Crossing 1500 5000 13.03778584° E 45.19661376° N Overtaking 500 5000 5000 32 33 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 13.23046709° E 45.20537200° N Overtaking 500 14.42333919° E 45.23865331° N Crossing 1500 15000 5000 12.41419925° E 45.32798734° N Allision 1000 10000 5000 12.99924959° E 45.35951700° N Crossing 1500 13.19543414° E 45.37878513° N Crossing 1500 30000 15000 13.05530232° E 45.40681149° N Crossing 1500 12.42470914° E 44.49945797°N Allision 1500 5000 5000 2.6 Discussion Shipping is perhaps the most international of the world’s major industries - and potentially one of the most dangerous. It has always been recognized that the best way to improve safety at sea is to develop international rules that are followed by all shipping nations. The traffic, density, and classification of maritime traffic in the North Adriatic have been presented in detail in this part of the study. A stochastic model has been applied to the extended area of the Gulf of Trieste, which is undoubtedly the riskiest area due to the numerous shipping lanes, the presence of a large number of hazardous substances, the shallow sea and the sensitive coastal zone. The results obtained are then extrapolated to the whole of the northern Adriatic using an experimental method. The predicted quantities of fuel and/or cargo discharged were also given. The probability of a major accident occurring during this period is estimated to be once every 120 years. A more accurate estimate would be obtained by IWRAP by analyzing a larger amount of AIS data, but the time and personnel cost for it exceeded NAMIRS funds and timetable. Thus, such an analysis is planned in the future. 34 35 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 3 / Oil spill simulations and exposure estimation 3.1 Introduction 3 This section is devoted to the presentation of the oil spill simulations performed for the definition of the exposures (Task 2) of the area of interest, in the context of the Activity 2.1 – Environmental Risk Assessment of NAMIRS. The Lagrangian particle tracking model LTRANS-Zlev (Laurent et al., 2020) including the oil spill module OILTRANS was used to perform the oil spill simulations coupled with an MITgcm (Massachusetts Institute of Technology general circulation model, (Marshall et al., 1997) implementation for the Adriatic Sea (Querin et al., 2016), employing an evolution of the model (Silverstri et al. ,2020), and applied in other studies (Melaku Canu et al., 2015; Bruschi et al., 2021) and research projects (Bendoni et al., 2022, Canu et al., 2022). Oil spill simulations 3.2 Oil spill simulations set up In section 2.5 three types of oil were identified as major potential threats for the area of interest: bunker oil, diesel oil, and crude oil. In order to obtain a statistical representation and exposure of the exposure, multiple oil spill simulations were performed from different release coordinates and repeated in time once a day every day for a one-year period (2018). Every oil spill simulation consisted in the release of 200 Lagrangian particles, advected by currents and wind drift using a Runge-Kutta scheme of 2nd order for a 10 day long time interval with an additional horizontal turbulent diffusivity of 10 m2/s and stranding along the estimation coast whenever the particles would approach at less than 10 meters from the border of the basin. The oil spill module OILTRANS computed the weathering processes to which the oil was subject, among them the initial spreading of the oil slick, the evaporation, the emulsification, and the vertical dispersion in the water column. For every oil type (bunker, diesel, crude) the simulations were performed modeling oil spill releases according to the estimations made in section 2.5 of the position and volumes of oil susceptible to be released at the different coordinates. Two sets of oil spill simulations were performed, distinguished by the method obtained to define the oil spill release positions. In the first set of simulations 54 potential oil spill release sites were simulated, the coordinates and volume of the oil spills that were modeled are defined in Table 4: they were determined using the stochastic method described in section 2.5. The second set of simulations is made by 33 potential oil spill release sites, the coordinates and volume of those oil spills are defined in Table 5, they were obtained using an extrapolated qualitative approach based on expert knowledge. 36 37 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment At every potential incident site, among those 54+33 sites, the quantity of bunker, crude, 3.3 Oil spill simulations results and diesel oil susceptible to be released, as estimated in Tables 4-5, were used to define the quantity of oil to be released in the simulations. Every type of oil was modeled using The results of the oil spill simulations allow to assess, for every oil type and every set of a specific parameterization of the OILTRANS module, as described in Table 6: the specific simulations, as presented in Fig. 36, the average volume of oil remaining on the surface, oils to model were identified thanks to SIOT (Società Italiana per l’Oleodotto Transalpi-stranding on surface, dispersed in the water column, and stranded at depth, within the no S.p.A.), the main operator in the transportation of oil and oil products in the whole water column. One can see that bunker and diesel oil behave in similar ways, with a rel-Mediterranean Sea, based in Trieste harbor, and providing a big portion of oil demand atively slow dispersion in the water column resulting in a quantity of stranded oil more in Central Europe through the Transalpine Pipeline. After the stakeholders’ workshop in important on the surface respect to the quantity of oil stranded at depth. Crude oil tends Trieste, SIOT provided information on the most common oils travelling in the Northern instead to disperse faster in the water column resulting in almost identical quantities of Adriatic Sea and some of their characteristics, which were complemented from literature oil stranded on the surface respect to the oil stranded at depth. (see Table 6). SIOT’s help was much appreciated and instrumental for obtaining good results for NAMIRS. Table 6: Model parameterization of the different oil types. These parameters are those of the specific oils “Bunker C Fuel Oil 171”, “Diesel Fuel Oil (1994) 242 & 254” and crude oil “Arabian light 46” taken from the Canadian catalogue of Crude Oil and Oil Product Properties (Jokuty et Al., 1999, revised 2022). The Fingas evaporation equation of type 1 is %Evap = (A + B × Tw)× ln( t) × (1 – Wc ) while type 2 is %Evap = (A + B × Tw ) × sqrt( t) × (1 – Wc) where Tw is the water temperature, t is time and Wc is the water content. a) Stochastic bunker oil average volume d) Expert bunker oil average volume bunker diesel crude API 11.4 37.2 19.66 Dynamic viscosity at 15°C 8.706 4.5 0.014 SARA asphaltenes content 11% 0% 3% SARA resin content 17% 2% 6% b) Stochastic diesel oil average volume e) Expert diesel oil average volume SARA saturated compounds 25% 76% 51% Evaporation Fingas A parameter 0.31 0.31 2.52 Evaporation Fingas B parameter 0.045 0.018 0.037 Evaporation Fingas equation type 1 2 1 The total number of oil spill simulations that were run varies according to the type of oil, as not all oil types were identified as threats on every release site. The number of oil spill c) Stochastic crude oil average volume f) Expert crude oil average volume simulations that were run are 19440 for the stochastic bunker oil, 19440 for the stochastic diesel oil, 17280 for the stochastic crude oil, 11880 for the expert bunker oil, 9360 for the expert diesel oil and 5400 for the expert crude oil. For every set of simulations and Figure 36: Average volumes of oil remaining on the surface (continuous blue line), stranding on surface every oil type the results were aggregated by summing the oil quantities in every cell of (dashed blue line), dispersed in the water column (continuous red line) and stranded in the water column (dashed red line), for every set of simulations (Stochastic and Expert) and every oil type (bunker, the domain, taking as initial time the instant of the release of every oil spill. diesel and crude). 38 39 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The oil spill simulations allowed to produce maps of oil density every 3 hours after the Instead, after 2 days of transport the threat extends along the Italian coasts up to the Po release. In order to give an overview of the results, we chose to present in this report in River delta. Regarding the surface stranded oil, the major exposure remains along the Figs. 37-38 (respectively for the Stochastic and Expert sets of simulations) only the bun-coasts of the Gulf of Trieste, while at depth larger quantities of oil stranded along the ker oil maps at two time-instants: 11 hours and 23 hours after the release of the oil spills. coastal areas between Venice and the Po River delta. These maps allow to identify which open sea areas and which coastal areas are more susceptible to be impacted by the oil spills, according to the threat defined by the release sites and oil volumes defined in Tables 4-5. Figure 38: Stochastic set of simulations for the bunker oil type at 11 hours (top) and 47 hours(bottom) after the release of the oil spills. Figure 37: Stochastic set of simulations for the bunker oil type at 11 hours (top) and 47 hours(bottom) Fig. 38 shows that potential bunker oil slicks released along the main traffic routes of the after the release of the oil spills. northern Adriatic (Expert set of release sites) represent a threat (both on surface and at depth) at short time interval (11 hours) for local costal segments, namely between Venice and the Po River, close to the Italian cities of Ravenna and Ancona, and on the north-The Figure 37 highlights that potential bunker oil slicks in the Gulf of Trieste (Stochastic ern part of the Croatian Island of Cres and the continental area closer to it next to Rijeka. set of release sites) represents a threat limited to the Gulf of Trieste itself for the first 11 Two days after the release, instead, most of the northern Italian coasts are concerned by hours after a potential incident, with oil stranding mainly on the surface respect to the oil stranded oil (except for the Gulf of Trieste) and in Croatia the threat extends along all the stranded at depth. Istrian peninsula from Rijeka to Savudrija. 40 41 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 4 / Stakeholders’ involvement 4.1 Introduction One of the main goals of NAMIRS 2.1 was the assessment of the vulnerability of coastal 4 areas of the Northern Adriatic Sea. This can be done in a purely objective manner by conducting scientific research on which types of coasts are the most sensitive to oil spills (e.g., ESI, see Petersen et al., 2019). However, such approach would neglect the subjective value represented by the coast for the different stakeholders, i.e., for those who have a direct, tangible or intangible interest in the areas to remain unaffected by the consequences of oil spills. The stakeholders can have an interest because they engage in economic activities in the coastal areas (maritime transportation, harbour activities, tourism, mariculture, fishery, etc.), because they value the pristine state of the area (i.e., consider its social, cultural, landscape values), or because they are engaged in activities related to environmental protection or cleaning, either as part of NGOs, public bodies (research institutes, local government), or of private enterprises in this sector. Stakeholders’ Thus, the partners of NAMIRS choose an inclusive, participative, holistic approach to the assessment of the coastal areas’ vulnerability in the Northern Adriatic Sea, by combining expert knowledge with the stakeholders’ involvement. In particular, a specific Coastal Vulnerability Assessment (CVA) method was developed in the form of a questionnaire to be compiled by stakeholders during purposedly organized workshops. The CVA was involvement developed according to the guidelines of the Delphi method, applied in the IALA PAWSA risk assessment method (IALA, 2022). Since the goals of the risk assessment in NAMIRS is different than the one addressed in the PAWSA methos, the CVA procedures were adapted to the specific needs of this project. The outcomes of the workshops were processed and joined with expert knowledge from literature in order to permit to classify the coastal areas based on their vulnerability to oil spills. GIS maps of vulnerability indexes related to the different considered vulnerability factors were also produced. Coastal vulnerability estimations will help in establishing priority areas for intervention in case of oil spills. 4.2 NAMIRS vulnerability factors The first step in the CVA procedure was the identification of the vulnerability factors. Three different factors of vulnerability were identified: • Geomorphological factors • Environmental factors • Socioeconomic factors 42 43 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Geomorphological factors are related to the typology of the coast. Different stakeholders 4.3 CVA questionnaire structure can give a different value to a coastal stretch, depending on the possible use there are making of them: e.g., some may value the coast for its recreational potential, outside of established beach resorts, others as place for an economic activity (hotel, camping, 4.3.1 CVA Step 1 restaurant). Thus, if a coast is low or high, easily reachable or inaccessible, sandy or rocky, natural or artificial, it all plays a role in the value assigned to it by a potential stake-Step 1 of the CVA asked participants to indicate their role in the assessment process, holder. their role in oil spill cleaning activities in general, and their level of familiarity with vulnerability factors. This resulted in a numerical value for the participant’s level of knowledge Environmental factors are related to important environmental features such as protected for each of the available factors. areas, important habitats, presence of protected or important species. Also in this case Roles were divided into the following categories: we expect that the value assigned by different categories of stakeholders, based on their interest and knowledge, may be different. For instance, people form the research sector • environmental association and NGO; or working in environmentalist organizations will have a different consideration for the • business sector employer (fishermen, shipping, touristic facilities etc.); protected areas and for their different level of protection. An interesting outcome will be • scientist, professor, or teacher; also the valuation for coastal areas without any type of legislative protection. • civil servant/elected official; Socioeconomic factors are those most dependent on the subjective perception of the • citizen. stakeholders. For a mariculturist any possible disruption of mariculture activities would Then, the participants had to state how would they participate in oil spill clean-up be seen as much more impacting then the pollution of a beach. On the other hand, the operations, should an oil spill occur, by selecting one of the following options: owner of a beach resort might value much more the protection of the beach, than of the mussel or fish farms in front of it. Even more complex is the evaluation of factors that are • would be in action per his/her duty; not measured in monetary terms: the aesthetic value of a pristine, natural coastline, or • would step in action only if requested; the cultural and historical value associated to the seafront of an old seaside town, might • would only actively monitor the cleaning procedures not be easily transformed in monetary value. Fig. 39 shows a schematic representation and offer suggestions or proposals to an appropriate service; of the three groups of vulnerability factors considered for NAMIRS CVA process. • would monitor the event as a citizen; • would not be interested. Level of familiarity with each factor was determined by asking the participants to answer the following questions with scores from 1 to 9, the former presenting the lowest level of familiarity, while latter presents the highest: • To what extent are you, in this moment, familiar with the problem of oil spills as a whole? • To what extent are you, in this moment, familiar with the geomorphological state of our coast (relating to different coast type cleaning difficulty)? • To what extent are you, in this moment, familiar with environmental protection (areas with different protection status levels)? • To what extent are you, in this moment, familiar with oil spill cleaning and intervention technology? • To what extent are you familiar with the socio-economic value of the various stretches of coastline dedicated either to tourism, recreation, mariculture, cultural heritage, economy, etc.? Figure 39: Schematic representation of the vulnerability factors considered in NAMIRS CVA. 44 45 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The goal of Step 1 questions was to allow the research team to possibly take into account Secondly, the participants were asked to assess the values of different types of coasts. the expertise and familiarity of each of the respondent. Obviously, the opinion of a perso-The following coast types were identified in the Northern Adriatic Sea based on informa-na professionally involved in oil spill management outweighs the opinion expressed by a tion from the EMODnet portal (see Section 5): person with a lower level of involvement or knowledge in the issue. After completing the self-assessment step, the participants were shown a navigational • Erodible rock with sediments at the base chart with 12 areas, where an oil spill is most likely to occur due to high traffic density and • Extended beaches (> 1 km) the presence of either drilling platforms, LNG terminals, etc. They were asked to choose • Small beaches (< 1 km) three areas where they believed an oil spill is most likely to occur. • Artificial coastline Participants could choose between the following 12 areas: • Muddy coastline • Non-erodible rock without sediments at the base 1. Trieste anchorage • Harbour area 2. Crude oil terminal SIOT - Trieste Finally, the participants were asked to evaluate the vulnerability of environmental factors, 3. Koper anchorage i.e., considering the protection status of the coastal areas in the Northern Adriatic Sea. 4. Rijeka anchorage as well as JANAF and INA terminals The following categories of protections status have been identified for NAMIRS goals, 5. Venice anchorage considering the different terminology and protections levels in use in the three North 6. Vela Vrata Adriatic countries: 7. LNG terminal Rovigo 8. Southern entrance/exit to/from separation scheme • National Parks and Marine Protected Areas 9. Northern entrance/exit to/from separation scheme • Natura 2000 and special protection areas 10. Separation triangle in the Gulf of Trieste • Unprotected areas 11. Platforms in North Adriatic near to the coast • Regional parks and Landscape parks 12. Platforms in North Adriatic between separation zones • Protected habitats or areas of presence of protected species 13. Other (please mark on the chart) In order not to influence the evaluation of the participants, the different categories of Please note that while Croatian workshops took place, the 4th option was solely repre-protection were not ordered according to an increasing or decreasing level of legislative sented by Rijeka anchorage without INA and JANAF terminals. After one of the partic-protection. ipants of Croatian workshop added INA terminal under the optional 13th answer, the research team decided to add those two terminals to the 4th answer. The decision to join them with Rijeka anchorage is due to their close proximity to each other. 4.3.3 CVA Step 3 Step 3 asked participants to state their comparative importance rating of each group of vulnerability factors with scores from 1 to 9, which were then converted to percentage 4.3.2 CVA Step 2 ratios. In Step 2 of the CVA participants were asked to rate the values assigned to each vulnerability factor using scores from 1 to 9, where 1 represents the lowest level of vulnerability, and 9 the highest level of vulnerability. Firstly, the participants were asked to assess the vulnerability of the socioeconomic factors (see Fig. 39) that may be affected by an oil spill, which include tourism, cultural heritage, cooling water stations, ports, recreational areas (man-made structures built along the coast for sports and other recreational activities), and maricultures (without distinc-tion for fish farms, shellfish farms, or other types of mariculture). Ports were further divided into commercial (e.g., Trieste, Koper, Rijeka), tourist (marinas), and local ports (i.e., small harbours typically found in old seaside towns, which are used by local fishermen or local owners of pleasure boats). 46 47 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 4.4 Stakeholders’ workshops Among non-governmental stakeholders, the following were invited: Three stakeholders’ workshops were organized, each one in a different partner country, • Private companies for oil spill prevention and clean-up in order to involve as much as possible, the local stakeholders into the process of risk • Oil companies management. • Touristic offices The workshops were organised by three project partners: UL FPP from Slovenia, OGS • Faculty of Maritime Studies of the University of Rijeka from Italy, and ATRAC from Croatia, with the goal to obtain subjective estimations of • Municipalities coastal vulnerability on the shores of the Northern Adriatic Sea. The participants met • National parks at each of the organised workshops either live in-situ, or online via a provided link to • Nature parks the digital version of the CVA questionnaire. The workshops proceeded in a completely The workshop started with a presentation from ATRAC’s director, Vedran Martinić, who anonymous way but with known participants. at the beginning shortly presented ATRAC and its activities. He then proceeded with explaining the ESI index, different types of the coast, and specificities of the Croatian coastline. He gave a few examples of case studies that happened during the years in 4.4.1 Workshop in Croatia which the coastline was heavily polluted, and of the techniques that were used for clean-The workshop for the Croatian stakeholders was organized by ATRAC on September 29th, ing the specific coast. Then, Valter Suban (UL-FPP) explained the questionnaire as well 2022, at the ATRAC premises in Rijeka. as its purpose and led the compilation by the participants. The workshop ended with a The stakeholder mapping for the Croatian workshop was done by compiling all the pre-discussion from the participants which opened some interesting questions about our vious contacts ATRAC has collected during its work in the relevant sector. It included all capabilities in case of a major oil spill. governmental and non-governmental entities, public and private sectors that are engaged in environmental protection activities and oil spill prevention and clean-up. That list was then reduced to stakeholders that could benefit from the NAMIRS project or their input was important to the project’s goals. The invitation for the workshop was sent by e-mail. Among the governmental stakeholders, the following were invited to participate: • Ministry of Maritime Affairs, Transport and Infrastructure • Ministry of Environmental Protection and Energy • Ministry of the Interior • Istra County • Primorje – Gorski kotar county • Lika – Senj County • Zadar county • Croatian Hydrocarbon Agency • Croatian Coast Guard • State Audit Office • Šibenik – Knin County • Port Authorities • Civil protection Figure 40: Two photographs from the Croatian stakeholders’ workshop in Rijeka at ATRAC premises, on September 29th, 2022. 48 49 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 4.4.2 Workshop in Slovenia Among the economic operators, the following were invited to participate: The workshop for the Slovenian stakeholders was organized by UL-FPP on October 6th, • Petrol (Fuel company) 2022, at the UL-FPP premises in Portorož. • Luka Koper INPO Stakeholder mapping for Slovenian Coastal Vulnerability workshop was undertaken by • TGZ Portorož (Tourism) means of identifying all governmental and non-governmental, public and private services • Adria Tow company and societies that either engage in environmental protection activities, run a business • Piloti Koper (harbour pilots) with a social or economic value, or otherwise deal with oil spill prevention and clean- • Among the non-governmental stakeholders, the following were invited: up. The research team first identified four important classes of tasks related to oil spill • Morigenos (NGO for marine mammal monitoring and protection) detection and clean-up, i.e., prevention, preparedness and monitoring activities (PPM); • DOPPS (NGO for birds monitoring and protection) detection and alerting tasks (DA); cleaning and cleaning-related activities (CCRA); post • PINA cleaning operations (PCO). Any service which engages in any of the tasks falling in either • Trinity of the four classes, was suitable for participation in the Coastal Vulnerability Assessment Workshop. Since UL-FPP is familiar with all official services who engage abovementioned activities, most of the governmental stakeholders were contacted by a telephone call or were sent an official invitation by e-mail. Most of the non-governmental stakeholders, however, were contacted and invited via e-mail only. All contact addresses were found online on each of the stakeholder’s web pages. Among the governmental stakeholders, the following were invited to participate: • Ministry of Infrastructure (Slovenian Maritime Administration) • Ministry of Defense (Administration for Civil Protection and Disaster Relief) • Ministry of Agriculture, Forestry and Fisheries (Fishery Inspection) • Ministry of Environment and Spatial Planning (Slovenian Environmental Agency (ARSO), VGP Drava, Institute for Water of the Republic of Slovenia, Slovenian Water Agency) • Ministry of the Interior (Police) • Ministry of Health (National Institute for Public Health) • Ministry of Finances (Financial Administration) Among research and educational stakeholders, the following were invited to participate: • National Institute for Biology • Coastal municipalities • Gymnasium, electro and nautical school Piran • Turistica (University of the Littoral - Faculty of Tourism Studies) • FHŠ (University of the Littoral - Faculty of humanistic studies) • University of the Littoral - Biotechnical faculty • Maritime museum Piran Figure 41: Two photographs from the Slovenian stakeholders’ workshop in Portorož at UL-FPP premises, • Managers of coastal and marine protected areas on October 6th, 2022. 50 51 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The workshop was organised live on the premises of UL-FPP and online via a prear-that are in Italy organized on a regional level, civil protection, and public authorities pro-ranged link to a Zoom meeting. All the important stakeholders (first team responders, en-moting economy (e.g., FLAC and GAC), which are usually promoted either by regional vironmental protection agencies, etc.) were present, with only a couple of stakeholders government or by the most important municipalities. In the Public authorities category who engage in touristic activates being absent. The workshop started with the presen-also other authorities were included, such as the port authorities of the major ports in tations from Valter Suban (UL-FPP), Vinko Bandelj (OGS), and Vedran Martinić (ATRAC) the area, the firefighters, the Coast Guard (also one of the partners in NAMIRS), and the relating to coastal clean-up, to the oil spill problem in general, and to the importance of Italian Court of Auditors. Furthermore, the CEI – Central European Initiative, an intergov-vulnerability mapping. Before participants started filling out the questionnaire for coastal ernmental organization promoting collaboration in the wider central European area was vulnerability evaluation, Valter Suban gave them a quick presentation on the structure of also invited as stakeholder: while CEI is also the LP of NAMIRS, it is not actively involved the questionnaire and the meaning behind its questions. in the activities of 2.1 (as it is not the Italian Coast Guard), thus no conflict of interest was detected. The total number of stakeholders in this category was 34, but for many of them several different possible contacts were identified. In some cases, the contacts 4.4.3 Workshop in Italy were of front offices or public relation offices, in other cases we tried to identify the ad-The workshop for the Italian stakeholders was organized by OGS on October 13th, 2022, ministrative structure of the public authority that might be most interested into NAMIRS at the OGS premises in Via Beirut 2 in Trieste. goals (e.g., environmental, tourism or economic regional directorates, or relevant city The stakeholder mapping for the Italian workshop was done starting from different lists councilors). of stakeholders that OGS already compiled in several past projects: HarmoNIA (Harmo-The stakeholders for the category Research and environmental services were the easiest nization and Networking for Contaminant Assessment in the Ionian and Adriatic, Seas, to be identified because of the many interactions that OGS has with similar institutions. EU ADRION, 2018-2019), ADRIREEF (Innovative exploitation of Adriatic Reefs in order to Thus, the possible stakeholders to invite were identified based on personal relationships strengthen blue economy, EU Interreg Italy – Croatia, 2018-2021), FAIRSEA (Fisheries in of members of the OGS NAMIRS workgroup with other researchers and scientists, on the Adriatic region - a Shared Ecosystem Approach, EU Interreg Italy - Croatia, 2019-2021), past participations in common projects with similar goals and objectives, and on the SHAREMED (Sharing and Enhancing Capabilities to Address Environmental Threats in existing lists of stakeholders of the projects cited above. We included in this category Mediterranean Sea, EU Interreg-MED, 2019-2022). Since each one of these projects had a public research institutions such as universities and research institutes, but also private different objective and goals, and thus possibly a different set of interested stakeholders, research institutes, cooperative for environmental services, as well as companies for the lists were pruned of all the stakeholders that might not be relevant for NAMIRS and environmental services. Cooperatives for environmental services often employ scientist complemented with other stakeholders in order to satisfy NAMIRS goals. who are great experts for local environmental features and can provide consultancy ser-The area of interest for the Italian stakeholders’ mapping was the entire Northern Adri-vices to local authorities in dealing with environmental problems, participate in moni-atic coast of Italy, from region Marche to region Friuli-Venezia Giulia. Four categories toring programs and in scientific projects. Companies for environmental services are of stakeholders were deemed interesting for NAMIRS purposes: Public authorities, Re-economic players and should be put in the category of Economy sector, but they also search and environmental services, Protected areas managers and NGOs, and Econo-provide services in case of oil spill, such as consultancy and cleaning service, and sell my sector. All four categories can be involved in the management of the oil spill, of the specific equipment for intervention in case of oil spill. A rather crude way of explaining cleaning and restoration measures that need to take place after an oil spill or can rep-the rationale for the inclusion of the environmental companies in the category Research resent stakeholders impacted by the consequences of an oil spill. The four categories and environmental services is that these companies have a positive impact from an oil reflect the “roles” that the participants had to assign themselves to in CVA Step 1. For all spill (because this is their core business), while the companies included in the catego-stakeholders cited below the roles and contact information of the contact persons were ry Economy sector are those that are generally negatively impacted by an oil spill (i.e., searched online on publicly accessible websites of the stakeholders or extracted from tourism, productive activities, mariculture and fishery operators). The total number of the existing lists of stakeholders of the projects cited above. When appropriate, more stakeholders identified in this category was 27, but for many of them several different than one email address was contacted for each stakeholder. persons were contacted. This was the case, e.g., for the University of Trieste (6 employees contacted) and the CNR-ISMAR institute of Venice (8 employees contacted). In the category Public authorities, we listed local (coastal municipalities) and regional (four regions: Marche, Emilia Romagna, Veneto, Friuli-Venezia Giulia) territorial public In the category Protected areas managers and NGOs we included stakeholders that are authorities: they are those whose officials are elected by citizens and are the direct re-involved in the management of protected areas, in environmental protection, or in envi-sponsible for the local and regional policies, including environmental issues and eco-ronmental education. Many of these are organizations with national and local offices, nomic sector. Thus, we included here also the environmental protection agencies (ARPA) and where possible both were contacted: this was the case, e.g., of WWF, Marevivo, LIPU. 52 53 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Some of them are generalist environmental organizations, others more specifically deal-Table 7: Breakdown of the Italian stakeholders contacted and present at the workshop in Trieste per ing with the protection of marine ecosystems, and possibly also involved in monitoring category of stakeholder. or citizen science projects, such as DelTa, Dolphin Biology and Conservation, Fondazione Cetacea. We included in this category also nautical societies, sport fishing associations, Public Research and Protected areas Economy and scuba diving clubs (including the association of the Italian scientific scuba divers authorities environmental managers and sector Total AIOSS). All these stakeholders may be strongly impacted by the consequences of an oil services NGOs spill but can also be seen as sentinels distributed along the coastline that have a day-to-Contacted day knowledge of the state of the sea, and their members may be counted on as possible stakeholders 34 27 42 36 139 volunteers to join operations after an oil spill. The total number of stakeholders in this Contacted category was 42 and also in this case, more than one email address was contacted when emails 65 57 49 38 209 needed. Participating The last category, Economy sector, was devoted to economy operators in the field of stakeholder 5 4 7 3 19 tourism, nautical sector, and fishery and aquaculture. All these activities are in general negatively impacted by an oil spill. Among the nautical sector stakeholders there were Participating people 5 10 8 3 26 very big players, such as Fincantieri, the biggest Italian ship building company, Ocean s.r.l, provider of marine services on a local and regional level, as well as the regional Maritime Technology Cluster FVG, and the nautical engineering company MICAD. In the touristic sector we contacted several associations of touristic operators (hotels, camping The workshop opened with a welcome from the director of the Oceanography section facilities) in the coastal areas of the Northern Adriatic Sea, including beach resorts and of OGS, Cosimo Solidoro, who also briefly introduced the institute and in particular its marinas. Fishery and aquaculture operators were the most abundant stakeholders repre-Oceanography Section. Anna Marconato (CEI), project leader of NAMIRS, presented the sented in this category, due to the small size and huge number of these operators in the project, its goals and mission. Followed a talk by Vinko Bandelj presenting the work being area, and also due to many contacts that OGS has already established with this sector done in NAMIRS 2.1 activity Environmental Risk Assessment. Valter Suban presented in past projects. One of the most important stakeholders in this group was undoubtedly the partner UL-FPP and its institutional activities, while Vedran Martinić presented the SIOT (Società Italiana per l’Oleodotto Transalpino S.p.A.), who is the main operator in the partner ATRAC and its main activities in the field of oil spill cleaning. After a coffee break, transportation of oil and oil products in the whole Mediterranean Sea, based in Trieste Fabrizio Gianni and Serena Zunino (both OGS) led the compilation of CVA questionnaires, harbor, providing a big portion of oil demand in Central Europe through the Transalpine by presenting the questions and illustrating them with figures and photographs for better Pipeline. The total number of stakeholders in this category was 36. understanding. The workshop ended with talks by Dario Giaiotti (ARPA-FVG), illustrating FIRESPILL (a project with many overlapping with NAMIRS), Riccardo Scottu (DESMI Ro-The total number of contacted Italian stakeholders across all four categories was 139. Clean A/S), presenting his company and its services for oil spill prevention and cleaning, An email inviting them to attend the NAMIRS Italian workshop, explaining the goals of and Donata Canu (OGS), presenting the ECHO group of OGS and its main scientific ex-the project and the structure and goal of the workshop, was sent to 209 email addresses. pertise. Some of the addresses turned out inactive or unreachable, thus additional research was performed in order to find a valid email for these stakeholders, but not for all was this successful. At the workshop 17 people participated in presence representing 12 different stakeholders, while 9 people, representing 7 other stakeholders, participated online. Table 7 shows the breakdown per category of the participants and stakeholders contacted and present at the NAMIRS workshop for the Italian stakeholders. At the workshop were also present members of the OGS NAMIRS workgroup, and representatives of partners in the project UL-FPP, ATRAC, CEI and of the Italian Coast Guard. Tables 8-11 cite all contacted stakeholders for each stakeholder category. 54 55 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Table 8: Stakeholders invited to the Italian workshop in the category of Public authorities. Comune 19 San Michele Local authority 2 PUBLIC AUTHORITIES al Tagliamento N Name Role Contacts Partecipated 20 Comune Lignano Sabbiadoro Local authority 2 1 Regione FVG Regional authority 4 21 Comune Grado Local authority 2 2 Regione Veneto Regional authority 3 22 Comune 3 Regione Monfalcone Local authority 1 Emilia-Romagna Regional authority 3 Comune Duino- 4 Regione Marche Regional authority 3 23 Aurisina – Občina Local authority 2 5 ARPA FVG Regional environmental agency 4 yes Devin-Nabrežina 6 ARPA Veneto Regional environmental agency 2 24 Comune Trieste Local authority 4 7 ARPA ER Regional environmental agency 2 25 Vigili del Fuoco Firefighters 2 yes 8 ARPA Marche Regional environmental agency 1 26 Comune di Staranzano Local authority 1 yes 9 Protezione civile Civil protection of 4 regions 4 Aries - Azienda 10 Corte dei Conti Court of Auditors 1 speciale della 27 Camera di Public authority promoting 11 Guardia Costiera economy 1 Capitanerie di Porto Coast guard 4 yes Commercio di Trieste Autorità di sistema 12 portuale del Mare Port authority 1 28 FLAG GAC Public authority promoting Adriatico Orientale Friuli Venezia Giulia economy 1 Autorità di sistema 29 Vegal Venezia Public authority promoting 13 portuale del Nord Port authority 1 Orientale economy 1 Adriatico 30 FLAG GAC Chioggia Public authority promoting Autorità di sistema e Delta del Po economy 1 14 portuale del Mare Delta 2000 - Gruppo Adriatico centro- Port authority 1 31 di Azione Locale Public authority promoting settentrionale Emilia-Romagna economy 1 Autorità di sistema 15 portuale del Mare Port authority 2 32 G.A.C. Marche Nord Public authority promoting economy 1 Adriatico centrale 33 FLAG GAC Marche Public authority promoting 16 Comune Centro economy 1 Cesenatico Local authority 1 Intergovernamental 17 Comune Rimini Local authority 1 34 CEI – Central European Initiative organization 1 yes 18 Comune Chioggia Local authority 3 56 57 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Table 9: Stakeholders invited to the Italian workshop in the category of Research and environmental services. RESEARCH AND ENVIRONMENTAL SERVICES Environmental services 18 GreenSea 1 N Name Role Contacts Partecipated company 1 OGS Public research institute 3 yes Environmental education 19 Cooperativa Sestante di Venezia 1 cooperative 2 CNR-ISMAR Trieste Public research institute 2 yes Environmental education 20 Hyla Società 1 3 CNR-ISMAR Cooperativa cooperative Venezia Public research institute 8 Consorzio 4 ISPRA - Chioggia Public research institute 5 21 mediterraneo Research consortium 1 5 Università di Trieste Public university 7 yes s.c.a.r.l. 6 Università di Venezia 22 Fondazione ENI - Ca' Foscari Public university 3 Enrico Mattei Foundation 2 Environmental services 7 Università 23 DESMI 1 yes di Padova Public university 2 company Environmental services 8 Università 24 LaFornitrice 1 di Bologna Public university 2 company 9 Politecnico Marche Public university 2 Environmental services 25 Istituto Delta – Ecologia applicata 1 company 10 Rete LTER-Italia Monitoring network 1 Environmental services 11 CORILA Research consortium 1 26 Garbage Service Srl 2 company Environmental services 12 Thetis S.p.A. 1 Environmental services company 27 t-ELIKA 1 company Environmental education 13 SELC Società Cooperativa 2 cooperative Shoreline - Soc. Environmental education 14 Coop. servizi per la qualità dell’ambiente 1 cooperative marino Environmental education 15 Cestha 1 cooperative Environmental education 16 Esplora s.r.l. 1 cooperative Environmental services com- 17 GRUPPO C.S.A. S.P.A. 1 pany 58 59 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Table 10: Stakeholders invited to the Italian workshop in the category of Protected areas managers and NGOs. PROTECTED AREAS MANAGERS AND NGOS 15 Amici della Terra Environmental NGO 1 N Name Role Contacts Partecipated 16 Legambiente Environmental NGO 1 1 AMP Miramare Protected area 1 yes 17 Greenpeace Environmental NGO 1 Riserva naturale 18 Italia Nostra Environmental NGO 1 2 regionale delle Protected area 1 Falesie di Duino 19 Mareamico Environmental NGO 1 Riserva naturale 20 Marevivo Environmental NGO 2 3 Foce Isonzo – Isola Protected area 1 21 FAI Environmental NGO 1 della Cona 22 LIPU Environmental NGO 2 Associazione per la 4 Laguna di Caorle e Protected area 1 23 WWF Italia Environmental NGO 3 Bibione 24 Oceanomare 5 Parco del Sile Protected area 1 Delphis Environmental NGO 1 6 Parco Lagunare Protected area 1 25 Fondazione Cetacea Environmental NGO 1 Associazione 7 Naturalistica Protected area 1 DelTa (Delfini e Cavallino 26 Tartarughe dell’alto Environmental NGO 1 yes Adriatico) 8 Tegnùe di Caorle P.to Falconera Protected area 1 27 Dolphin Biology and Conservation Environmental NGO 1 9 Associazione tegnue di Chioggia Protected area 1 Associazione 28 "Comitato per la Environmental NGO 1 10 Delta Po Veneto casa dei pesci" Parco regionale Protected area 1 AIOSS Ente Parco Delta 29 Associazione italiana Scientific scuba divers 11 del Po Emilia-Ro- Protected area 3 operatori scientifici association 1 yes magna subacquei Ente Parco FIPSAS Sport fishing association, 12 Naturale Regionale Protected area 1 30 Federazione Italiana recognized as environmental 1 del Conero Pesca sportiva ed protection association by the Attività Subacquee Ministry 13 Associazione Paguro Protected area 1 31 Lega Navale Italiana Nautical association 1 yes Adriapan - Adriatic 32 Assonautica Nautical association 1 14 Protected Areas Protected area 2 33 ARCI PESCA FISA Sport fishing association 1 Network 60 61 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 34 Barcolana Nautical association 1 Table 11: Stakeholders invited to the Italian workshop in the category of Economy sector. 35 SVBG Nautical association 1 ECONOMY SECTOR 36 Sirena Nautical association 1 N Name Role Contacts Partecipated 37 Čupa Nautical association 1 Associazione 38 Murena diving club Scuba divers association 1 yes 1 Riviera del Conero Tourism 1 e Colli dell'Infinito 39 Acquamission diving club Scuba divers association 1 MARITIME Circolo 2 TECHNOLOGY Nautical sector 1 40 sommozzatori Scuba divers association 1 CLUSTER FVG trieste s.c.a.r.l. 41 Scuba tortuga Scuba divers association 1 3 Fincantieri Nautical sector 1 42 Club del gommone Nautical association 1 4 Samer Seaports & Terminals Nautical sector 1 SIOT - Trieste 5 Italian Society for the Transalpine Oil transport company 1 yes Pipeline 6 Ocean s.r.l. Nautical sector 1 7 Cooperativa spiagge Ravenna Tourism 1 8 Destinazione Turistica Romagna Tourism 1 9 Porto turistico di Jesolo Tourism 1 10 Portopiccolo Tourism 1 11 Pro Loco Marina di Ravenna Tourism 1 12 Ravenna Incoming Tourism 1 13 Società Gestione Campeggi Tourism 1 14 AGCI FVG Fishery & aquaculture 1 AMA 15 Associazione Mediterranea Fishery & aquaculture 1 Acquacoltori 62 63 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 16 API (Associazione 32 Coop Copego Fishery & aquaculture 2 Piscicoltori Italiani) Fishery & aquaculture 1 Anapi pesca CO.VE.P.A. - (Associazione 17 Consorzio Veneto Fishery & aquaculture 1 33 Nazionale Autono- Fishery & aquaculture 2 Pesca Artigianale ma Piccoli Impren- ditori della pesca) Co.Ge.Mo. Monfal- cone - Consorzio 34 FINALMAR Fishery & aquaculture 2 18 gestione pesca Fishery & aquaculture 1 compartimento 35 MICAD Nautical sector 1 yes di Monfalcone 36 Master Blue Growth Tourism 1 yes COGIUMAR - 19 Consorzio giuliano Fishery & aquaculture 1 maricoltori 20 Federcoopesca Fishery & aquaculture 1 21 Federpesca Fishery & aquaculture 1 22 LegaCoopFVG Fishery & aquaculture 1 23 Legapesca Fishery & aquaculture 1 Organizzazione 24 di Produttori della Pesca di fasolari Fishery & aquaculture 1 dell'Alto Adriatico Unci pesca (Unione Nazionale 25 Cooperative Fishery & aquaculture 1 Italiane della Pesca e Acquacoltura) 26 CO.GE.MO Fishery & aquaculture 1 27 CO.GE.VO Chioggia Fishery & aquaculture 1 28 CO.GE.VO. Venezia Fishery & aquaculture 1 Organizzazione di 29 Produttori Bivalvia Fishery & aquaculture 1 Veneto S.C. 30 Cooperativa Casa del Pescatore Fishery & aquaculture 1 31 Cooperativa Figure 42: Two photographs from the Italian stakeholders’ workshop in Trieste at OGS premises, on Adriatica Tourism 1 October 13th, 2022. 64 65 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 4.5 Workshops results and analysis of outcomes The self-assessment of the 104 participants gave as a result 7 representatives of environmental organisations and other NGOs, 13 members of business sector, 30 scientists, This Section contains the results obtained from the three organised workshops in Rijeka, professors, or teachers, 48 civil servants or elected officials, 5 citizens, while one partic-Portorož and Trieste, where in total 104 people participated either in-situ or online via ipant did not answer to this question. This is shown in Fig. 43, with numbers related to provided links to the digital versions of the questionnaire. The research team processed answers through the following legend: the obtained results to gather raw statistical information from each of the workshops, which will be subjected to further processing in the following phases of NAMIRS. • 1 = environmental association and NGO; For easier understanding of the question list in the column on the left of the following • 2 = business sector employees (fishermen, shipping, touristic facilities etc.); tables, please refer to the following legend: • 3 = scientist, professor, or teacher; • 4 = civil servant/elected official; • Q4a – Socio-economic factors/mariculture • 5 = citizen. • Q4b – Socio-economic factors/tourism • Q4c – Socio-economic factors/recreation • Q4d – Socio-economic factors/cultural heritage • Q4e – Socio-economic factors/cooling water stations • Q4f – Socio-economic factors/commercial ports • Q4g – Socio-economic factors/tourist ports • Q4h – Socio-economic factors/local ports • Q5a – Geomorphology/erodible rock with sediments at the base • Q5b – Geomorphology/extended beaches (> 1 km) • Q5c – Geomorphology/small beaches (< 1 km) • Q5d – Geomorphology/artificial coastline • Q5e – Geomorphology/muddy coastline • Q5f – Geomorphology/non-erodible rock without sediments at the base • Q5g – Geomorphology/harbour area • Q6a – Environment/national parks and marine protected areas • Q6b – Environment/Natura 2000 and special protected areas • Q6c – Environment/unprotected areas Figure 43: Breakdown of the self-assessed role of the participants to all three workshops. • Q6d – Environment/regional parks and landscape parks • Q6e – Environment/protected habitats or areas of presence of protected species • Q7a – Comparison/socioeconomic factors The participants were asked to state their task in the case of an oil spill event. The chart • Q7b – Comparison/environmental factors in Fig. 44 shows a distribution of participants by their stated tasks, based on the follow- • Q7c – Comparison/geomorphological factors ing legend: • Q7d – Comparison/socioeconomic factors to % • 1 = would be in action per his/her duty; • Q7e – Comparison/environmental factors to % • 2 = would step in action only if requested; • Q7f – Comparison/geomorphological factors to % • 3 = would only actively monitor the cleaning procedures and offer suggestions The raw statistical data showcased in the tables are coloured in shades of green, yellow or proposals to an appropriate service; and red. Numbers marked in green represent the lowest level of vulnerability, yellow ones • 4 = would monitor the event as a citizen; represent a medium level of vulnerability, and red ones a high level. • 5 = would not be interested. From a total of 104 participants, there were 35 people who would be in action per their duty, 23 people who would step in action if requested, 24 people who would actively monitor the situation and offer suggestions to appropriate services, 21 people who would monitor the event as a citizen, and no persons who would not be interested. 66 67 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Table 12: Main statistics for vulnerability scores provided by participants to the live Slovenian workshop for each vulnerability factor: Average, Mode, Median, Standard deviation. In the leftmost column the corresponding question of the CVA, see legend in Section 4.5. Question Average Mode Median Standard deviation Q4a 6,8 9 8 2,5 Q4b 7,1 9 7 2,1 Q4c 5,6 5 6 1,7 Q4d 6,4 7 7 1,5 Q4e 4,9 3 5 2,4 Q4f 6,1 6 6 2,0 Q4g 6,4 8 7 2,0 Figure 44: Breakdown of the self-assessed task in case of oil spill of the participants to all three workshops Q4h 6,6 7 7 1,5 Q5a 7,1 9 8 2,5 Q5b 6,3 8 8 2,7 4.5.1 Slovenian results Q5c 6,8 9 8 2,3 A total of 54 people participated in the Slovenian CVA workshop, 14 of them live and 40 Q5d 5,0 6 6 2,4 online. The self-assessment of the roles of the participants gave the following results: 29 civil servants or elected officials, 3 members of business sector, 16 scientists, pro-Q5e 6,0 7 7 2,3 fessors, or teachers, 3 members of environmental organisations or other NGOs, and 3 Q5f 6,8 8 8 2,2 citizens. Q5g 5,9 6 6 2,2 Q6a 7,8 9 8 1,6 Live Q6b 7,8 9 8 1,6 In total, 14 people participated to the workshop in-situ. Of them 9 identified themselves Q6c 6,7 7 7 1,7 as civil servants or elected officials, 1 as member of business sector, and 4 as scientists, Q6d 7,6 9 8 1,5 professors, or teachers. Table 12 showcases the main statistics of vulnerability scores Q6e provided by participants for each vulnerability factor, indicated in the left column. 7,9 9 9 1,6 Q7a 7,4 7 8 1,5 Q7b 8,0 9 9 1,6 Q7c 6,9 8 7 1,6 Q7d 33% 29% 33% / Q7e 36% 38% 37% / Q7f 31% 33% 30% / 68 69 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment On average, the participants of the live workshop gave the highest priority to various Table 13: Main statistics for vulnerability scores provided by participants to the online Slovenian workshop protected areas in the environmental vulnerability factors group, while lesser importance for each vulnerability factor: Average, Mode, Median, Standard deviation. In the leftmost column the corresponding question of the CVA, see legend in Section 4.5. was given to geomorphological and socio-economic factors, although there were no major differences between them. However, it must be mentioned that the scores were very dispersed as it is indicated by the standard deviation. Question Average Mode Median Standard deviation The results for the question on the areas of highest oil spill probability at the live Slove-Q4a 7,2 8 8 2,0 nian workshop are shown on the chart in Fig. 45. To interpret the chart correctly, please refer to the areas list in Section 4.3.1 of this report. Q4b 7,0 7 7 1,7 Q4c 5,9 6 6 2,0 Q4d 6,0 7 6 2,0 Q4e 5,6 5 6 1,6 Q4f 5,7 6 6 2,2 Q4g 5,9 6 6 2,0 Q4h 5,8 6 6 2,0 Q5a 8,1 9 9 1,3 Q5b 7,6 9 8 1,8 Q5c 6,1 7 7 2,4 Q5d 5,1 5 5 2,2 Q5e 6,6 7 7 1,9 Q5f 7,0 9 7 2,2 Q5g 4,8 3 5 2,3 Figure 38: Most probable oil spill locations as selected by the participants to the live Slovenian workshop. Q6a 7,8 9 8 1,7 Q6b 7,7 9 8 1,6 Online Q6c 6,9 7 7 1,6 A total of 40 people participated in the online workshop. Among them 20 identified them-Q6d 7,3 7 7 1,7 selves as civil servants or elected officials, 2 as members of business sector, 12 as sci-Q6e 7,7 9 8 1,6 entists, professors, or teachers, 3 as members of environmental organisations or other Q7a 6,9 7 7 1,5 NGOs, and 3 as citizens. Table 13 showcases the main statistics of vulnerability scores provided by participants for each vulnerability factor, indicated in the left column. Q7b 8,0 9 9 1,3 Q7c 7,3 8 8 1,7 Q7d 31% 29% 29% / Q7e 36% 38% 38% / Q7f 33% 33% 33% / 70 71 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The results of the online workshop very closely resemble those of the live workshop, the Table 14: Main statistics for vulnerability scores provided by participants to the Croatian workshop for each only difference being in geomorphology which was preferred over socio-economic fac-vulnerability factor: Average, Mode, Median, Standard deviation. In the leftmost column the corresponding question of the CVA, see legend in Section 4.5. tors. As it is indicated by the standard deviation, the scores were very dispersed. The results for the question on the areas of highest oil spill probability at the online Slovenian workshop are shown on the chart in Fig. 46. To interpret the chart correctly, please Question Average Mode Median Standard deviation refer to the areas list in the Section 4.3.1 of this report. Q4a 8,6 9 9 0,7 Q4b 8,3 9 9 0,8 Q4c 7,4 9 8 1,5 Q4d 6,9 9 7 1,7 Q4e 6,1 6 6 1,6 Q4f 6,8 7 7 1,9 Q4g 7,5 9 8 1,8 Q4h 6,6 7 7 2,2 Q5a 7,0 8 8 1,0 Q5b 8,0 9 9 1,4 Q5c 7,4 8 8 1,5 Q5d 5,3 5 5 2,0 Q5e 6,3 6 6 2,1 Figure 46: Most probable oil spill locations as selected by the participants to the online Slovenian workshop Q5f 4,7 5 5 1,9 Q5g 5,5 7 6 2,4 Q6a 8,8 9 9 0,6 4.5.2 Croatian results Q6b 8,3 9 8 0,7 A total of 24 people participated in the Croatian CVA workshop, which was held only live Q6c 7,5 9 8 1,3 in Rijeka. Q6d 7,8 8 8 1,2 Among those who participated 16 identified themselves as civil servants or elected officials, 2 as scientists, professors, or teachers, and 6 as members of the business sector. Q6e 8,5 9 9 1,0 Table 14 showcases the main statistics of vulnerability scores provided by the partici-Q7d 32% 30% 30% / pants for each vulnerability factor, indicated in the left column. Last three lines, however, Q7e 39% 40% 40% / slightly differ from the Slovenian and Italian ones. This is because the first version of the CVA questionnaire did not ask participants to compare the three factors with scores Q7f 29% 20% 30% / from 1 to 9, but solely with a percentage ratio. While the results between all three workshops are comparable, because the scores can be transformed to percentage ratios, the Croatian ones did not provide the participants’ general assessment of importance (e. g., if a participant would give scores 3, 3, 3, to three vulnerability factors, and another scores 9, 9, 9, the percentage scores would be in the same ratio of 33%, 33% and 33%, but it is clear that the second participant deems all three factors to be much more important than the first one). 72 73 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The results of the Croatian workshop show the highest assessed importance being given Table 15: Main statistics for vulnerability scores provided by participants to the live Italian workshop to environmental and socioeconomic factors, while geomorphological factors were not for each vulnerability factor: Average, Mode, Median, Standard deviation. In the leftmost column the corresponding question of the CVA, see legend in Section 4.5. estimated to be as important. As was the case with Slovenian workshop results, the Croatian ones show high dispersion too. The results for the question on the areas of highest oil spill probability at the Croatian Question Average Mode Median Standard deviation workshop are shown on the chart in Fig. 47. To interpret the chart correctly, please refer Q4a 8,3 9 9 1,3 to the areas list in the Section 4.3.1 of this report. Q4b 8,1 9 9 1,0 Q4c 7,6 9 8 1,6 Q4d 7,3 7 7 1,3 Q4e 5,5 6 6 2,0 Q4f 6,6 9 7 2,4 Q4g 7,4 9 8 1,4 Q4h 6,9 9 7 1,8 Q5a 7,3 8 8 1,8 Q5b 8,1 9 8 1,3 Q5c 7,8 7 8 1,2 Q5d 5,8 6 6 1,9 Q5e 7,7 9 9 1,9 Q5f 6,6 6 7 2,1 Figure 47: Most probable oil spill locations as selected by the participants to the Croatian workshop. Q5g 5,6 9 6 3,0 Q6a 8,5 9 9 0,8 Q6b 8,3 9 9 1,4 4.5.3 Italian results Q6c 6,9 7 7 1,2 A total of 26 people participated in the Italian CVA workshop, 10 of them online and 16 Q6d 8,3 9 9 1,0 live. Among them, 12 identified themselves as scientists, professors, or teachers, 4 as members of business sector, 3 as civil servants or elected officials, 4 as members of Q6e 8,4 9 9 1,3 environmental organisations or other NGOs, 2 as citizens, and one participant did not Q7a 7,5 7 8 1,1 answer to this question. Q7b 8,7 9 9 0,5 Q7c 7,4 7 7 1,4 Live Q7d 32% 30% 32% / The workshop was conducted at the same time live as well as online. Of a total of 26 par-Q7e 37% 39% 38% / ticipants, 16 people participated live. Among them 7 identified themselves as scientists, Q7f 31% 30% 30% / professors, or teachers, 2 as members of business sector, 1 as civil servant or elected official, 1 as citizen, and 1 did not answer this question. Table 15 showcases the main statistics for each vulnerability factor, indicated in the left column. 74 75 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The participants gave the highest priority to environmental factors, while socio-econom-Table 16: Main statistics for vulnerability scores provided by participants to the online Italian workshop ic and geomorphological factors were estimated to be almost equally important. Once for each vulnerability factor: Average, Mode, Median, Standard deviation. In the leftmost column the corresponding question of the CVA, see legend in Section 4.5. again standard deviation highlights high score dispersion. The results for the question on the areas of highest oil spill probability at the live Italian workshop are shown on the chart in Fig. 48. To interpret the chart correctly, please refer Question Average Mode Median Standard deviation to the areas list in the Section 4.3.1 of this report. Q4a 8,3 9 9 0,8 Q4b 7,4 8 8 1,1 Q4c 6,9 8 8 1,6 Q4d 7,2 7 7 1,0 Q4e 6,4 7 7 1,3 Q4f 5,3 6 6 1,8 Q4g 6,3 7 7 1,9 Q4h 6,0 6 6 2,0 Q5a 6,7 8 7 1,9 Q5b 7,3 9 8 1,6 Q5c 7,8 7 8 0,8 Q5d 4,3 6 4 1,9 Q5e 7,0 8 8 1,4 Figure 48: Most probable oil spill locations as selected by the participants to the live Italian workshop. Q5f 6,9 8 8 1,9 Q5g 4,1 2 4 2,3 Online Q6a 8,4 9 9 0,8 From the total of 26 participants, 10 participated online. Among them were 5 scientists, Q6b 7,9 9 9 1,4 professors, or teachers, 1 civilian, 2 members of business sector and 2 civil servants or Q6c 7,3 7 7 1,4 elected officials. As was the case in the tables showcased above, the one below provides Q6d 8,3 9 9 1,0 the same information. Q6e 8,8 9 9 0,4 Q7a 7,8 8 8 1,1 Q7b 8,5 9 9 1,3 Q7c 7,0 7 7 1,3 Q7d 33% 33% 33% / Q7e 36% 38% 38% / Q7f 30% 29% 29% / 76 77 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Unlike in the previous workshops, the participants of the online Italian workshop gave a 4.5.4 Joint workshop results higher priority to socio-economic factors then to geomorphology, while environmental factors were assessed in the same manner as before and given the top priority. However, Table 17 shows the joint results of the three organised stakeholders’ workshops. the scores were highly dispersed as it is shown by standard deviation values. The results for the question on the areas of highest oil spill probability for those partic-Table 17: Main statistics for vulnerability scores provided by participants to all three stakeholders’ ipating online to the Italian workshop are shown on the chart in Fig. 49. To interpret the workshops for each vulnerability factor: Average, Mode, Median, Standard deviation. In the leftmost column the corresponding question of the CVA, see legend in Section 4.5. chart correctly, please refer to the areas list in the Section 4.3.1 of this report. Question Average Mode Median Standard deviation Q4a 7,8 9 8,8 1,8 Q4b 7,6 9 8 1,5 Q4c 6,7 7 8 1,9 Q4d 6,8 7 7 1,7 Q4e 5,7 5 6 1,8 Q4f 6,1 6 7 2,1 Q4g 6,7 8 7 1,9 Q4h 6,4 7 7 2,0 Q5a 7,2 9 8 1,8 Q5b 7,5 9 8 1,8 Q5c 7,2 7 8 2,0 Figure 49: Most probable oil spill locations as selected by the participants to the online Italian workshop. Q5d 5,1 6 5 2,1 Q5e 6,7 9 7 2,0 Q5f 6,4 7 7 2,2 Q5g 5,2 5 6 2,5 Q6a 8,2 9 9 1,3 Q6b 8,0 9 8 1,4 Q6c 7,1 7 7 1,4 Q6d 7,8 9 8,5 1,5 Q6e 8,3 9 9 1,4 Q7a 7,4 7 8 1,4 Q7b 8,3 9 9 1,2 Q7c 7,1 8 7 1,5 Q7d 32% 29% 32% / Q7e 36% 38% 38% / Q7f 31% 33% 30% / 78 79 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The joint results highlight that the highest priority was given to the environmental factors, Table 18: Main statistics for vulnerability scores of each vulnerability factor provided by participants that while socio-economic and geomorphological factors were given similar levels of priority self-assessed as members of Environmental organizations of NGOs in all three stakeholders’ workshops: Average, Mode, Median, Standard deviation. In the leftmost column the corresponding question of the with the slight advantage of the former. CVA, see legend in Section 4.5. The research team also separately calculated measures of central tendency and standard deviation for each of the self-assessed categories of stakeholders. In this way, possible differences in the answers due to the different background of the participants can ENVIRONMENTAL ORGANISATION OR NGO be assessed. Question Average Mode Median Standard deviation Table 18 shows joint results of answers provided by members belonging to either envi-Q4a 7,4 9 9 2,8 ronmental organisations or other NGOs. Q4b 7,6 9 9 2,2 Q4c 7,3 9 9 2,6 Q4d 7,0 9 8 2,6 Q4e 4,6 6 5 1,6 Q4f 4,7 5 5 2,1 Q4g 6,4 6 6 2,3 Q4h 6,3 6 6 2,2 Q5a 7,3 9 8 2,3 Q5b 5,9 8 7 2,9 Q5c 6,7 9 8 3,1 Q5d 4,7 2 5 2,4 Q5e 8,6 9 9 0,8 Q5f 6,9 9 7 2,5 Q5g 2,3 1 2 1,6 Q6a 7,7 9 9 2,6 Q6b 8,0 9 9 2,6 Q6c 7,4 9 8 1,9 Q6d 7,6 9 9 2,6 Q6e 7,7 9 9 2,6 Q7a 7,1 7 7 1,6 Q7b 8,7 9 9 0,7 Q7c 6,9 8 8 1,9 Q7d 31% 29% 29% / Q7e 38% 38% 38% / Q7f 30% 33% 33% / 80 81 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Table 19 shows joint results of answers provided by employees of the maritime business Table 20 shows joint results of answers provided by those that in the three workshops sector. identified themselves as scientists, professors or teachers. Table 19: Main statistics for vulnerability scores of each vulnerability factor provided by participants that Table 20: Main statistics for vulnerability scores of each vulnerability factor provided by participants that self-assessed as members of the Business sector in all three stakeholders’ workshops: Average, Mode, self-assessed as Scientist, professor or teacher in all three stakeholders’ workshops: Average, Mode, Median, Standard deviation. In the leftmost column the corresponding question of the CVA, see legend in Median, Standard deviation. In the leftmost column the corresponding question of the CVA, see legend Section 4.5. in Section 4.5. BUSINESS SECTOR SCIENTIST, PROFESSOR OR TEACHER Question Average Mode Median Standard deviation Question Average Mode Median Standard deviation Q4a 8,2 9 9 1,2 Q4a 7,8 9 8 1,7 Q4b 7,9 9 9 1,5 Q4b 7,2 7 7 1,5 Q4c 6,7 9 7 2,0 Q4c 6,2 7 7 2,0 Q4d 6,9 7 7 1,5 Q4d 6,2 7 7 1,8 Q4e 5,5 3 6 2,1 Q4e 5,9 5 6 1,4 Q4f 7,4 9 8 2,1 Q4f 5,6 6 6 2,4 Q4g 7,0 9 8 2,1 Q4g 6,3 6 7 1,9 Q4h 6,8 9 7 2,2 Q4h 6,0 6 6 2,0 Q5a 7,3 8 8 1,4 Q5a 7,8 9 8 1,6 Q5b 7,8 9 9 1,6 Q5b 7,4 8 8 1,9 Q5c 7,2 9 8 1,9 Q5c 6,6 7 7 1,8 Q5d 5,0 3 5 2,5 Q5d 4,8 6 5 2,2 Q5e 6,3 5 6 1,7 Q5e 6,8 9 7 2,0 Q5f 5,8 8 6 2,4 Q5f 6,8 7 7 2,2 Q5g 5,3 7 6 2,6 Q5g 5,0 3 5 2,5 Q6a 8,2 9 9 1,2 Q6a 7,9 9 8 1,3 Q6b 7,7 8 8 1,4 Q6b 7,8 9 8 1,3 Q6c 6,9 5 7 1,5 Q6c 6,8 7 7 1,2 Q6d 7,7 9 8 1,4 Q6d 7,3 8 8 1,4 Q6e 8,5 9 9 0,9 Q6e 8,0 9 9 1,4 Q7a 7,1 9 7 1,8 Q7a 6,8 7 7 1,5 Q7b 8,3 9 9 1,1 Q7b 8,0 9 9 1,3 Q7c 8,0 9 8 1,1 Q7c 7,0 7 7 1,8 Q7d 30% 33% 29% / Q7d 31% 30% 31% / Q7e 35% 33% 38% / Q7e 37% 39% 38% / Q7f 34% 33% 33% / Q7f 32% 30% 31% / 82 83 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Table 21 shows joint results of answers provided by participants to all three workshop Table 22 shows the joint results of answers provided by participants to all three work-who self-assessed as civil servants or elected officials. shop who self-assessed as citizens. Table 21: Main statistics for vulnerability scores of each vulnerability factor provided by participants that Table 22: Main statistics for vulnerability scores of each vulnerability factor provided by participants that self-assessed as Civil servant, elected official in all three stakeholders’ workshops: Average, Mode, Median, self-assessed as Citizen in all three stakeholders’ workshops: Average, Mode, Median, Standard deviation. Standard deviation. In the leftmost column the corresponding question of the CVA, see legend in Section 4.5. In the leftmost column the corresponding question of the CVA, see legend in Section 4.5. CIVIL SERVANT, ELECTED OFFICIAL CITIZEN Question Average Mode Median Standard deviation Question Average Mode Median Standard deviation Q4a 7,6 9 8 1,9 Q4a 8,0 8 8 0,7 Q4b 7,6 9 8 1,6 Q4b 7,2 7 7 1,1 Q4c 6,6 8 7 1,8 Q4c 6,8 7 7 0,8 Q4d 6,6 5 7 1,7 Q4d 7,0 7 7 0,7 Q4e 5,8 5 6 1,9 Q4e 5,4 7 6 2,1 Q4f 6,1 6 6 1,8 Q4f 7,2 7 7 0,9 Q4g 6,7 8 7 2,0 Q4g 7,0 7 7 1,2 Q4h 6,2 7 7 2,0 Q4h 6,6 7 7 1,1 Q5a 7,4 9 8 1,9 Q5a 6,6 9 8 2,9 Q5b 7,7 9 8 1,6 Q5b 8,4 9 9 0,9 Q5c 7,1 8 8 2,0 Q5c 7,2 7 7 2,0 Q5d 5,5 5 5 1,9 Q5d 5,2 4 4 2,8 Q5e 6,3 7 7 2,1 Q5e 7,0 8 8 2,0 Q5f 6,1 9 6 2,2 Q5f 7,2 9 8 2,0 Q5g 5,5 7 6 2,2 Q5g 6,2 5 5 2,7 Q6a 8,3 9 9 1,2 Q6a 8,8 9 9 0,4 Q6b 8,0 9 8 1,2 Q6b 8,8 9 9 0,4 Q6c 7,1 7 7 1,5 Q6c 7,6 7 7 1,3 Q6d 7,8 9 8 1,4 Q6d 8,6 9 9 0,9 Q6e 8,2 9 9 1,3 Q6e 8,6 9 9 0,9 Q7a 7,5 8 8 1,2 Q7a 7,4 8 8 1,5 Q7b 8,2 9 9 1,3 Q7b 8,2 9 9 1,3 Q7c 7,3 8 8 1,4 Q7c 7,2 7 7 0,4 Q7d 33% 32% 32% / Q7d 32% 33% 33% / Q7e 36% 36% 36% / Q7e 36% 38% 38% / Q7f 32% 32% 32% / Q7f 32% 29% 29% / 84 85 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment In Figs. 50-69 the participants’ rankings of the different vulnerability factors are shown as graph charts. Figure 50: Graph chart of the rankings assigned to Q4a vulnerability factor by participants to all three Figure 52: Graph chart of the rankings assigned to Q4c vulnerability factor by participants to all three workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = median score value. median score value. Figure 51: Graph chart of the rankings assigned to Q4b vulnerability factor by participants to all three Figure 53: Graph chart of the rankings assigned to Q4d vulnerability factor by participants to all three workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = median score value. median score value. 86 87 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Figure 54: Graph chart of the rankings assigned to Q4e vulnerability factor by participants to all three Figure 56: Graph chart of the rankings assigned to Q4g vulnerability factor by participants to all three workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = median score value. median score value. Figure 55: Graph chart of the rankings assigned to Q4f vulnerability factor by participants to all three Figure 57: Graph chart of the rankings assigned to Q4h vulnerability factor by participants to all three workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = median score value. median score value. 88 89 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Figure 58: Graph chart of the rankings assigned to Q5a vulnerability factor by participants to all three Figure 60: Graph chart of the rankings assigned to Q5c vulnerability factor by participants to all three workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = median score value. median score value. Figure 59: Graph chart of the rankings assigned to Q5b vulnerability factor by participants to all three Figure 61: Graph chart of the rankings assigned to Q52 vulnerability factor by participants to all three workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = median score value. median score value. 90 91 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Figure 62: Graph chart of the rankings assigned to Q5e vulnerability factor by participants to all three Figure 64: Graph chart of the rankings assigned to Q5g vulnerability factor by participants to all three workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = median score value. median score value. Figure 63: Graph chart of the rankings assigned to Q5f vulnerability factor by participants to all three Figure 65: Graph chart of the rankings assigned to Q6a vulnerability factor by participants to all three workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = median score value. median score value. 92 93 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Figure 66: Graph chart of the rankings assigned to Q6b vulnerability factor by participants to all three Figure 68: Graph chart of the rankings assigned to Q6d vulnerability factor by participants to all three workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = median score value. median score value. Figure 67: Graph chart of the rankings assigned to Q6c vulnerability factor by participants to all three Figure 69: Graph chart of the rankings assigned to Q6e vulnerability factor by participants to all three workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = workshops. Blue = score distribution, Green = average score value, Red = mode of score values, Yellow = median score value. median score value. 94 95 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The graphs show that most participants refrained from using the lowest available scores, 4.6 Discussion most likely due to not having a complete perspective over the importance of some of the vulnerability factor groups, which led to overestimation of the vulnerability for some The cumulative score distribution graphs of each vulnerability factor highlight how most factors. This is also highlighted by the overall standard deviation values which show participants refrained from using the whole available spectre of scores ranging from the high dispersion. In general, it can be observed that the highest rankings are those most lowest to the highest, but mostly used scores of the upper half, which can be interpreted favoured for all vulnerability factors. in two ways. In Fig. 70 the results on the most probable position of oil spill incidents in the Northern The first assumption based on the results would be that participants simply deem all the Adriatic Sea according to all the participants to the three workshops are shown. vulnerability factors to be very important and consequentially would not neglect any of them should an oil spill occur. Another hypothesis is that workshop participants lacked proper knowledge on some of the vulnerability factors, which resulted in overestimation. A better explanation from the organizers on the vulnerability factors might have enlarged the range of values used by the participants. Nevertheless, all participants belonged to a group of experts, and we can consider that in any case they had sufficient knowledge for an informed compilation of the questionnaires. The joint results highlighted that the participant stakeholders assigned the highest priority, with a share of 36%, to the environmental factors, while socio-economic and geomorphological factors were given similar levels of priority topping 32% and 31% respectively. Thus, all three groups of vulnerability factors were valued in a similar way from the stakeholders. More detailed analysis might bring in the spotlight possible different rankings assigned by the different categories of stakeholders. The result on the group of environmental vulnerability factors were in part surprising. Figure 70: Most probable oil spill locations as selected by the participants to all three stakeholders’ workshops. All categories of protection were assigned high vulnerability values by the stakeholders. This might have been the result of a conscious choice by the stakeholders. Nowadays the environmental conscience is very high and perhaps the stakeholders perceived that It can be seen that the participants perceived as the most dangerous areas the SIOT ter-no area, protected or unprotected, should be degraded by the consequences of an oil minal in Trieste, the Trieste anchorage, the separation triangle, and the Koper anchorage. spill. On the other hand, perhaps the communication from the organizers as to what Two participants indicated additional positions for incidents involving oil spills. The first are the unprotected areas was not sufficient. In fact, it seems as if the stakeholders did participant indicated the INA terminal in front of Rijeka (see 4.3.1: this choice was later assign a high vulnerability to unprotected areas precisely because they are not protect-included into the 4th option, so this area was chosen 28 times in total), while the other ed. With the same logic, a protected area, being already protected, was considered less did not suggest any alternative position. vulnerable to oil spills. This might have been addressed better by the organizers, since a In general, the results to this question were as expected, with two exceptions, first being pollution from and oil spill would degrade the protected and the unprotected areas in the the high number of times the SIOT terminal was selected, the second being the low num-same way. Nevertheless, inherent in the system of nature protection and conservation is ber of times the Venice anchorage was selected. In fact, SIOT terminal was the people’s the idea that areas need different protection according to their importance. Thus, areas first choice by a large margin, probably also because of the memories of the terrorist more deserving protection, get higher levels of protection (i.e., at the level of MPAs or Na-attack in 1972. On the other hand, the latter was chosen fewer times than expected, most tional Parks), while areas less deserving protection, get lower forms of protection (e.g., likely because there were not many participants from that region, but predominantly from Natura2000 or protected habitats according to one of the EU directives). The results of Slovenia, Croatia, and the FVG region in Italy. the stakeholders’ workshops might have been different if we chose to ask about the “value” or “importance” of the environmental factors, rather than about the “vulnerability”. In any case, the results for the group of environmental factors were too much com-pressed on the high end of the ranking: the lowest average value 7,1 was assigned to the category of unprotected areas, the highest 8,3 to protected habitats or areas of presence of protected species. To MPAs and National Parks, the highest levels of protection in Italy 96 97 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment and Croatia respectively, the average value of vulnerability assigned by the participants Adriatic Sea and their characteristics provided by one of the stakeholders, namely SIOT: was 8,2. If we used these values for the vulnerability index and vulnerability mapping, operating in the Trieste port this is Europe’s largest crude oil terminal in the Mediterra-simply all areas of the Northern Adriatic coastline would require very high priority in case nean. SIOT’s help was much appreciated and instrumental for obtaining good results of oil spills… This is not practical from the point of view of intervention in case of oil spill for NAMIRS. Another stakeholder that participated in the Italian workshop, the Marine and it is thus not useful in the frame of NAMIRS scope and goals. Thus, the research Protected Area of Miramare (the first MPA declared in Italy in 1986) asked OGS to help team decided not to use the results of the stakeholders’ workshops regarding the vulner-in the preparation of a specific contingency plan in case of oil spill for the area under ability of environmental factors for vulnerability indexes and mapping derivation. Instead, protection. Thus, NAMIRS is already outgrowing its scope. we opted to use an expert knowledge approach and to assign vulnerability rankings to Besides obtaining valuable information for the goals of NAMIRS, and besides the above-environmental factors proportionally to the level of legislative protection granted to each mentioned developments, and while not being the main goal of the workshops, there protected area. Thus, the MPAs and National Parks were rated highest, the unprotected were other benefits associated to the organization of the three stakeholders’ workshop. areas lowest, on a ranking from 1 to 9 (more on this in Section 5). One of them was the strengthening of the collaboration between NAMIRS partners, with For the group of geomorphological vulnerability factors, in the workshops we did not frequent meetings and coordination between the organizers from the three involved part-ask for “vulnerability” of the different types of coastlines, but for the “value” assigned ners (UL-FPP, OGS, ATRAC). Another is the outreach of the project and its goals to a wider to them by the stakeholders. This was done because the environmental vulnerability of public of persons, institution and companies, interested in the exploitation of the sea and the coastline is not a matter of subjective judgment, but of objective factors related to of the coastal areas. the substrate type (grain size, permeability, trafficability, mobility), slope of the shoreline, and its exposure to waves and tides. All these factors affect the fate of the oil particles that might reach the shore, as well as the cleaning methods to apply and their efficiency. Thus, in the vulnerability indexes and mapping, the stakeholders’ evaluation of geomorphological factors was merged with the socioeconomic factors, since both express the perceived, subjective value of the stakeholders. On the other hand, in the computation of the geomorphological vulnerability index, we used a modified version of the NOAA ESI (Petersen et al., 2019) instead. The ESI index is an objective evaluation of the difficulty of cleaning each cost type, originally developed for US coasts, but here adapted to the Northern Adriatic Sea coast typologies (see the next Section). The results of the stakeholders’ workshops thus provided the necessary information in order to proceed with the computation of the vulnerability indexes and the production of vulnerability maps for the Northern Adriatic Sea. Based on these, suggestions for the prioritization of protection and cleaning of different coastal areas can be included in contingency planning for emergencies in the Northern Adriatic Sea. A possible future improvement would be to apply the Delphi method of priority selection. In the question on the “familiarity” with the topics of the questionnaire, the participants had to self-assess their level of knowledge on each of the vulnerability factors. The scores of those who expressed higher familiarity level, would have greater importance than the scores of participants with a lesser level of familiarity. In the Delphi method the participants are divided into three levels of knowledge, and the input of each of them is then adjusted to match the corresponding knowledge level. In this way, the answers can be weighted by the expertise of the stakeholders and more weight is given to those that have higher expertise in a certain field. The involvement of the stakeholders already sparked some interesting developments. First of all, as already described in Section 3, the oil spill simulations set up was possible also thanks to the information on the most common oils travelling in the Northern 98 99 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 5 / Vulnerability mapping and assessment 5.1 Maps of vulnerability factors Rapid interventions of the Coast Guard and Response Teams and decisions on priority 5 areas to protect in case of an oil spill event, require detailed information and maps on coastal vulnerability that decision makers can easily and quickly consult. To provide high-resolution maps of different vulnerability factors (VFs) and the total coastal vulnerability in the Northern Adriatic Sea, georeferenced data on the presence of the selected VFs in NAMIRS (Socioeconomic VFs, Environmental VFs, and Geomorphological VFs; see Section 4), was obtained by previous projects we carried out in the area, by searching on online databases and asking to colleagues that could store such data. A list of the maps of the VFs for each VF group is reported in Table 23 with information on the source, the file format, and a brief description. Because most kinds of oil are less dense than water, most spilled oil floats on the wa-Vulnerability ter surface and it expects to affect mainly upper-sublittoral and surface organisms and structures. Thus, only VFs on the coastline and up to 5 meters depth were retained for the final coastal vulnerability assessment. The bathymetry chart of the Northern Adriatic Sea, used to clip all the maps, was derived from EMODnet – Bathymetry. In addition, a buffer of 3 nautical miles radius was drafted around the coastline to include only the VFs mapping and that are closer to the coast, being more prone to be affected by oil spill. Since the study area of the NAMIRS project includes only the marine portion of the Northern Adriatic Sea, coastal lagoons (i.e., the Venice lagoon and the Grado-Marano lagoon) were not considered in the analysis. Finally, to create the maps of the VFs and of the total coastal vulnerability a score was assessment assigned to each VF based on the mean scores given by the participants to the three workshops or according to our expertise. All operations and analyses on the maps and visualization were performed using the free and open source QGIS software with the WGS84 coordinate reference system (EPSG:4326). Socioeconomic VFs – The selected socioeconomic VFs included ‘Mariculture’, ‘Cultural heritage sites’, ‘Harbour areas’, ‘Recreational-touristic traits of coast’, and the ‘Value of coast by typology’ (Table 23). The ‘Mariculture’ VF map was downloaded by the Adriplan data portal (http://data.tools4msp.eu/) and include location of shellfish and fish farms (Fig. 71). The map of ‘Cultural heritage sites’ VF was created by merging information from maps of archaeological and paleontological sites available on EMODnet – Geology (www.emodnet-geology.eu/), EMODnet Human Activities (www.emodnet-human-activities.eu/), and on the Bioportal of Croatia (bioportal.hr). A 100 m radius buffer was created around each cultural heritage point with the aim to cover as much as possible the total extension of the sites (Fig. 72). To have a more realistic representation of the 100 101 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment ‘Harbour areas’ VF, the original map of points identifying the ports in the Northern Adriat-Table 23. List of the vulnerability factors used for the coastal vulnerability assessment. ic Sea, downloaded by the Adriplan data portal, was modified by adding additional ports after comparison with the Google Satellite map. Then, a 100 m, 250 m and 750 m radi-VF Type Source Description VF group us buffer were drawn around local ports, marinas and commercial ports, respectively, and an intersection with the Northern Adriatic Sea coastline was performed to obtain a Mariculture Shape Adriplan data Shellfish and Socio- (polygons) portal fish farms economic polyline shape file representing harbour areas (categorized in local ports, marinas, commercial ports) (Fig. 73). Since no maps reporting the recreational and touristic sites were EMODnet available, a joined map of the ‘Recreational-touristic traits of coast’ VF was generated Cultural Shape geology and Archaeological- Socio- by intersection between the Northern Adriatic Sea coastline and a 100 m radius buffer heritage (points) human activities; Paleontological economic drawn around the bathing water sites downloaded from EMODnet - Human Activities sites Bioportal Croatia sites (Fig. 74). Although the number of traits of coast with recreational-touristic activities may be underestimated using the information on bathing water sites, this was the only avail-Created in Commercial- able information that we could use as a proxy for deriving such VF. The ‘Value of coast by QGIS using the industrial ports, typology’ VF represents different geomorphological types of coasts, that expect to have coastline of local ports, marinas. Harbour Shape Northern Adriatic Further harbour Socio- a different value to the person who uses them for either environmental or socio-econom-areas (polylines) Sea, and a layer of areas were added to economic ic activities. The map of ‘Value of coast by typology’ was created by modifying a map ports downloaded the original file after of coastal typology in the Northern Adriatic Sea, download from EMODnet – Geology. from Adriplan data comparison with Nevertheless, different classifications were used by the countries in their entries on the portal Google Satellite coast typology in the Northern Adriatic Sea. In fact, some categories were present only in Italy, others only in Slovenia, still others only in Croatia. In many cases these catego-The coastline of ries across different countries were related to the same coast typology. Thus, the origi-Northern Adriatic nal coastal geomorphologies reported in this map were merged in seven categories for Sea is divided simplicity: erodible rock with sediments at the base, extended beaches (> 1 km), small in polylines and beaches (< 1 km), artificial coastline, muddy coastline, non-erodible rock without sedi-classified according ments at the base, and harbour area (Fig. 75). A value was then assigned to each of these Modified in QGIS to the different coastal typologies coastal typologies according to the results of the questionnaires (see paragraph 4 and Value of Shape from a layer of (e.g., muddy Socio- below). Although the ‘Cooling water stations’ VF was considered in the questionnaire coast by (polylines) coastal typology coastline, erosion- economic provided to the participants of the workshops, no information on the location of this VF typology downloaded from resistant coast, in the Northern Adriatic Sea was found, thus the cooling water stations were not included EMODnet geology harbour area). in the final coastal vulnerability map. A value is then assigned to each typology according to the results of the questionnaires Created in QGIS using the The layer reports Recreational- coastline of the traits of coast touristic Shape Northern Adriatic characterized Socio- traits of (polylines) Sea, and the by touristic and/ economic coast bathing water sites downloaded or recreational from EMODnet - activities Human Activities 102 103 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment VF Type Source Description VF group VF Type Source Description VF group National Prof. Annalisa parks and Shape Falace, National Distribution of Marine (polygons, Institute of Biology seagrasses Protected Shape points) of Slovenia, Areas of (polygons) Mapamed Environmental EMODnet Biology National Distribution legislation Shape (points) EMODnet Biology of Cladocora Regional caespitosa parks and Shape Falace et al 2015; Landscape (polygons) Mapamed Environmental Fortibuoni et al parks 2020; Gordini Shape and Ciriaco 2020; Distribution of rocky Natura 2000 (points) Ponti 2020; Prof. outcrops (‘ trezze’) and special Shape Annalisa Falace; protection (polygons) Mapamed Environmental Adriblu data portal areas Created in QGIS Protected by difference habitats between the Areas without the and areas Shape EMODnet Biology Distribution of map of Northern presence of any of presence (polygons, (www.emodnet- coralligenous Environmental Unprotected Shape Adriatic Sea kinds of protected Environmental of protected points) biology.eu/) and maerl areas (polygons) marine region and site or protected species the joined map of species and habitat EMODnet Biology; protected areas Shape Distribution of and species (points) updated by Emmanuelle Fucus virsoides The coastline of Created in QGIS Northern Adriatic Shape (points) EMODnet Biology Distribution of Cystoseira spp. using as a base Sea is divided the layer of in polylines and Shape Coast coastal typology classified according (polygons, Miramare MPA; Distribution cleaning Shape downloaded to the different Geo- points) EMODnet Biology of Pinna nobilis difficulty (polylines) from EMODnet - coastal typologies morphological Geology and the (e.g., muddy Distribution of ESI ranking of coastline, erosion- Shape (points) EMODnet Biology habitat-forming NOAA (Petersen resistant coast, invertebrates et al., 2019) harbour area) and ESI ranking of NOAA Shape Bioportal of Distribution of (points) Croatia biocenoses 104 105 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Figure 71. Map of the mariculture in the Northern Adriatic Sea. Figure 71. Map of the mariculture in the Northern Adriatic Sea. Figure 72. Map of the cultural heritage sites in the Northern Adriatic Sea. 106 107 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment To each socioeconomic VF the corresponding mean score as given by the participants to the three workshops was assigned. They ranged between 5.1 and 7.8 (Table 24). ‘Mariculture’ got the highest score, while the lowest value was assigned to the ‘artificial coastline’ category of the ‘Value of coast by typology’ VF. Table 24. Score assigned to each socioeconomic VF. Socioeconomic VF Category of the socioeconomic VF Score Mariculture 7.8 Cultural heritage sites 6.8 Recreational-touristic traits of coast 7.15 Local ports 6.4 Harbour areas Marinas 6.7 Figure 74. Map of the recreational-touristic traits of coast in the Northern Adriatic Sea. Commercial ports 6.1 Erodible rock with sediments at the base 7.2 Extended beaches (> 1 km) 7.5 Small beaches (< 1 km) 7.2 Value of coast by typology Artificial coastline 5.1 Muddy coastline 6.7 Non-erodible rock without sediments at the base 6.4 Harbour area 5.2 Figure 75. Map of the coastal typologies in the Northern Adriatic Sea. A value is then assigned to each typology according to the results of the questionnaires (not shown in the map). 108 109 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Environmental VFs – The environmental VF group included ‘National Parks and Marine Protected Areas (MPAs)’, ‘Regional and Landscape parks’, ‘Natura 2000 sites and special protection areas’, ‘Protected habitats or areas of presence of protected species’, and ‘Unprotected areas’ (Table 25). The maps of the protected areas in the Northern Adriatic Sea were downloaded from MAPAMED (www.mapamed.org), a database of marine protected areas in the Mediterranean Sea. Nature reserves, Natural monuments, National special reserves, and Landscape parks were included in the ‘Regional and Landscape parks’ VF. All parks designed under an international legislation (e.g., Ramsar sites, SPAMI) were considered ‘special protection areas’ and included in a VF with Natura 2000 sites. The maps with the distribution of protected species and habitats in the Northern Adriatic Sea were obtained from literature, Adriblu data portal and colleagues (Tab. 23). A 100 m radius buffer was created around the occurrence points of species and habitat as a better proxy of their presence. The ‘Unprotected areas’ VF was obtained by subtract-ing the surface area of the joined map of protected areas and protected species and habitats to the map of the whole Northern Adriatic Sea marine region (Fig. 76). Participants to the workshops assigned a mean score of 8.3 to protected species and habitats; 8.2 to national parks and MPAs; 8 to Natura 2000 and special protection areas; 7.8 to regional and landscape parks and 7.1 to unprotected areas. Since these scores would give a misleading result, increasing the importance of protected species and habitats respect to the Natura 2000 sites and regional parks, we decided to assign an arbi-trary score (from 1 to 9) to the environmental VFs based on the level of formal protection granted to the different categories of protected areas (Tab. 25). Table 25: Vulnerability scores assigned to each environmental VF. Environmental VF Score National parks and MPAs 9 Regional and landscape parks 7 Natura 2000 and special protection areas 5 Protected species and habitats 3 Unprotected areas 1 Figure 76. Map of the environmental VFs in the Northern Adriatic Sea. Details of the Gulf of Trieste and the coastline of the Kornati archipelago are also shown. 110 111 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Geomorphological VFs – Since every beach or coastline is composed of different materials, which respond to oil in different ways, geomorphology must be taken into account as well. To create a map with the information on the coast cleaning difficulty, coastal geomorphologies in the Northern Adriatic Sea, were downloaded from EMODnet – Geology. Nevertheless, different classifications were used by the countries in their entries on the coast typology in the Northern Adriatic Sea. In fact, some categories were present only in Italy, others only in Slovenia, still others only in Croatia. In many cases these categories across different countries were related to the same coast typology. These categories were compared and matched, as much as possible, Environmental Sensitivity Index (ESI) Table 26. Coastal typologies classified by sensitivity to oil via Environmental Sensitivity Index (ESI) values, that assess the coastal sensitivity to oil spill (https://response.restoration.noaa.gov/ defined by the NOAA, the modified ESI as applied in this project, and the new rank assigned to the coastal oil-and-chemical-spills/oil-spills/shoreline-sensitivity-rankings-list, Petersen et al., 2019) typologies for the coast cleaning difficulty map after being rescaled from 1 to 9. (Table 26, Fig. 77). For instance, the categories ‘Harbor area’ and ‘Coastal embarkment with construction’ were unified and matched with the NOAA category ‘Exposed solid man-made structures’, while all the categories that identify small and pocket beaches were unified and matched with the NOAA category ‘Mixed sand and gravel beaches’ (see Table 26 for a complete correspondence between our categories and those identified by NOAA). To each identified category, a score was assigned according to the ESI ranking. This ranking ranges from 1 to 10: higher values indicate greater sensitivity to oil spill, thus, in our case, a value of 10 indicates a higher coast cleaning difficulty. The lowest scores are assigned to the coastal typology categories that are easier to clean such as ‘Exposed rocky shores’ and ‘Exposed man-made structures’ (score 1) and ‘Fine to medium-grained sand beaches’ (score 3). Intermediate scores are assigned to ‘Mixed sand and gravel beaches’ (score 5), and to ‘Gravel beaches’ and ‘Riprap’ (score 6). A high score (8) is assigned to ‘Sheltered rocky rubble shores’. Since NOAA ESI attributes a rank from 7 to 10 to categories identifying estuaries and muddy coastlines (i.e., ‘Exposed tidal flats’, ‘Sheltered tidal flats’, ‘Vegetated low banks’, ‘Salt- and brackish-water marshes’), we assigned a rank of 9 to these coastal typologies in the Northern Adriatic Sea. The ESI ranks 2, 4, and 10 were not assigned in the NAMIRS ranking since the categories of Table 26. Coastal typologies classified by sensitivity to oil via Environmental Sensitivity Index (ESI) values, coastal geomorphology identified in the NOAA index with this score are not present in defined by the NOAA, the modified ESI as applied in this project, and the new rank assigned to the coastal the Northern Adriatic Sea (e.g., ‘Coarse-grained sand beaches’ - score 4, and ‘Inundated typologies for the coast cleaning difficulty map after being rescaled from 1 to 9. low-lying tundra’ - score 10). Since the NOAA ranking scale goes from 1 to 10, while the rank scale proposed in the questionnaires presented to the participants ranged between 1 and 9, we rescaled the assigned ranks according to this range. The new ranking is re-5.2 Mapping the coastal vulnerability ported in Table 26. All maps of the VFs were transformed from vector to raster format with a 100x100m cell size, to calculate the coastal vulnerability index. Before raster conversion, a 50 m radius buffer was drawn around the polyline shape files (i.e., ‘Value of coast by typology’, ‘Recreational-touristic traits of coast’, ‘Harbour areas’, ‘Coast cleaning difficulty’) to embrace the whole trait of coast. Separate vulnerability maps for each VF group were generated by overlapping all VF rasters and extracting the maximum value in each raster pixel (Figs. 78-80). Finally, a map of total coastal vulnerability was obtained with the same method (Fig. 81). 112 113 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Although this method presents some limitation as only one VF is considered in each pixel, it guarantees adequate decisions on the priority areas requiring intervention in case of oil spill, because the most vulnerable VFs are selected to create the vulnerability maps. Vulnerability scores of the maps were then categorized in four classes and visualized in GIS with different colours: very low vulnerability (1-2, green), low vulnerability (3-5, yellow), medium vulnerability (6-7, orange), high vulnerability (8-9, red). Figure 78. Maps of the coastal vulnerability for the socioeconomic VF group with details of the Gulf of Figure 79. Maps of the coastal vulnerability for the environmental VF group with details of the Gulf of Trieste and the coastline near Rijeka. The red color indicates higher coastal vulnerability. Trieste and the coastline near Rijeka. The red colour indicates higher coastal vulnerability. 114 115 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Figure 81. Maps of the total coastal vulnerability obtained by using information from all the selected VF Figure 80. Maps of the coastal vulnerability based on the coast cleaning difficulty with details of the Gulf groups. Details of the Gulf of Trieste and the coastline near Rijeka are also shown. The red color indicates of Trieste and the coastline near Rijeka. The red color indicates higher coast cleaning difficulty. higher coastal vulnerability.. 116 117 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 5.3 GIS project and map visualization All maps were included in a geopackage file for QGIS (Fig. 82). The GeoPackage open format is a container that allows to store GIS data (layers) in a single file. A single GeoPackage file can contain various data (both vector and raster data) in different coordinate reference systems, as well as tables without spatial information; all these features allow to share data easily and avoid file duplication. In the geopackage file created for the NAMIRS project, and named ‘NAMIRS CVA.gpkg’, vector and raster maps of all VFs were included, together with the vulnerability maps (both the total coastal vulnerability map and the vulnerability maps of the single VF groups). In the geopackage file, a QGIS project, named ‘NAMIRS project’ was also uploaded (Fig. 82). Once the project is opened in the QGIS layer panel, the vulnerability maps can be visualized divided by VF group and with different colours according to the four classes of the vulnerability scores (Fig. 83). Figure 83. A screenshot of the NAMIRS project opened in the QGIS layer panel (red box). The symbology of the VF group maps is categorized in four classes representing the different levels of coastal vulnerability. Figure 82. A screenshot of the geopackage file uploaded in QGIS with all the layers and the NAMIRS project (red box). 118 119 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 6 / Cumulative oil spill risk index 6.1 Introduction The term risk refers to the expected loss as a function of hazard, exposure and vulnera-6 bility (UNDRO, 1979; Cardona, 2005). Hazard is defined as a potentially damaging event which may cause the loss of biological organisms, environmental degradation, damages and degradation of structures with social and economic importance, and is strictly characterized by its location, intensity, frequency and probability (UNISDR, 2005). Exposure expresses how likely the receptors can be exposed to the abovementioned hazards. Thus, it depends on the type of hazard, on the mechanisms by which receptors can be impacted, and by their location. Vulnerability to a specific hazard is the propensity of the receptors (individuals, groups of people, species, habitats, ecosystems, but also social and economic systems, infrastructure, etc.) to be damaged if they are exposed to that hazard (Menoni et al. 2012). In literature there exist different conceptual frameworks for the identification and quan-Cumulative tification of the mutually interdependent concepts of Hazard-Exposure-Vulnerability-Risk (Landis 2004, Birkmann, 2007; Halpern et al., 2008). In this work, we followed the approaches developed in Menoni et al. (2012), Melaku Canu et al. (2015), Depellegrin et al. (2017), Menegon et al. (2018), Furlan et al. (2018), and in the projects HarmoNIA oil spill risk index (Harmonization and Networking for Contaminant Assessment in the Ionian and Adriatic, Seas, EU ADRION, 2018-2019) and SHAREMED (Sharing and Enhancing Capabilities to Address Environmental Threats in Mediterranean Sea, EU Interreg-MED, 2019-2022), adapting them to the specificities of NAMIRS. In particular, Task 1 (see Section 2) provided an estimate of the hazard related to oil spill in the Northern Adriatic Sea. The estimate was based on the analysis of the traffic in the area, considering routes, traffic density, type of vessels and their characteristics (length, speed, velocity, cargo). From the analysis we derived the information on the most probable location of incidents, the type of incidents (collision, allision, grounding), the type of vessels involved, and the type and quantities of possible oil spills. In Task 2 (see Section 3) the information on the oil spill hazard was used to derive the information on the exposure of the coastal environment to oil spills in Northern Adriatic Sea. This was done by simulating oil transformation, advection, dispersal, and stranding with an oil spill model based on a Lagrangian particle tracking model on top of a specific North Adriatic 3D hydrodynamic model with real-world forcings (meteorological conditions, river inflows). The quantities of simulated oil particles that reach the coast over different periods of time provide the exposure of coastal receptors. In Task 3 (see Sections 4-5) we identified the coastal receptors that can be impacted by an oil spill and assessed their vulnerability assigning weights through a combination of literature information, expert knowledge, and stakeholders’ involvement. 120 121 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment The last step, described in this Section, is the integration of the exposure maps with the All operations and analyses on the maps and visualization were performed using the vulnerability maps in order to obtain the final risk assessment of coastal areas for oil spill free and open source QGIS software with the WGS84 coordinate reference system in the Northern Adriatic Sea. These results will be then useful for contingency planning, (EPSG:4326). which is one of the main expected results of NAMIRS. Table 27. Computation of the percentage of bunker oil stranded after each hourly time step from the release. Time steps at which 30% and 50% of the released volume of oil is stranded on the coast are 6.2 Cumulative oil spill risk index highlighted. To calculate the risk index in NAMIRS, the maps of exposure related to the possible events of oil spill in the Northern Adriatic Sea (see Section 3) were multiplied to the maps of each vulnerability factor (environmental, geomorphological and socioeconomic) and the map of the total coastal vulnerability derived in Section 5. Prior to conducting the analysis, we summed up the values of the exposure maps generated using expert and stochastic methods for each oil type (bunker oil, crude oil, and diesel oil) and each time step after the simulated oil spill. This allowed us to encompass all potential sites of oil spill release in the Northern Adriatic Sea. Furthermore, we summed the values of the maps representing the average volume of oil remaining on the surface, stranding on surface, dispersed in the water column, and stranded at depth, in order to have a unique map for each oil type and time step after the release. The maps of dispersed oil in the water column were first clipped to 5 meters deep since we considered vulnerability factors from the surface up to this depth. For each type of oil, we then calculated the time it took for 30% and 50% of the oil released in each simulation to become stranded (at surface and in water column). To do that we divided the average volume of stranded oil (in m3/km2) in each time step by the total amount of oil released in each oil type simulation (Tables 27-29). The average volume of stranded oil in m3/km2 was obtained by summing the volume of oil in the map cells, multiplied by the area in km2 of the cells and divided by the number of releases. For the bunker oil, 30% of oil stranded in 68 hours after the release and 50% stranded in 131 hours after the release. For the crude oil, 30% and 50% of stranded oil was reached after 83 and 173 hours, respectively, while for the diesel oil, 30% of oil stranded in 83 hours and 50% in 185 hours from the release. Since the maps of the vulnerability factors range between 1 and 9 (see Section 5), to calculate the risk index the maps representing the 30% and 50% of stranded oil, for each oil type, were scaled at the same range of values. The minimum and maximum of the range (1 and 9) were matched with the minimum and maximum of the maps of 50% of stranded oil. After multiplying the hazard maps with the vulnerability factor maps and with the total coastal vulnerability map, a square root transformation was applied to convert the maps to the 1-9 range. To be in accordance with the vulnerability factor maps, risk maps were created up to 5 meters of bottom depth and up to 3 nautical miles from the coast (see Section 5). For the same reason only the marine portion of the Northern Adriatic Sea was considered while coastal lagoons (i.e., the Venice lagoon and the Grado-Marano lagoon) were kept out of the analysis. 122 123 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Table 28. Computation of the percentage of crude oil stranded after each hourly time step from the Table 29. Computation of the percentage of diesel oil stranded after each hourly step from the release. release. Time steps at which 30% and 50% of the released volume of oil is stranded on the coast are Time steps at which 30% and 50% of the released volume of oil is stranded on the coast are highlighted. highlighted. 124 125 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 6.3 Maps of risk index for oil spill in Northern Adriatic Sea The risk index calculated for the Northern Adriatic Sea showed that the areas with the highest risk are in the proximity of the Isonzo river mouth and Grado town, Trieste-Miramare coastline, Strunjan Landscape park and Debeli rtič in Slovenia and from Chioggia town to the Po Delta river. These areas are reached by the highest quantity of oil in case of incident and are particularly sensitive due to the presence of numerous recreational-touristic activities, protected species, such as Cymodocea nodosa, forming dense meadows at very shallow waters, protected areas (e.g., Miramare MPA, Strunjan Landscape park), aquaculture and muddy coastline that is very difficult to clean (see Section 5). Although the coastline of Veneto and Emilia-Romagna Regions had high values of vulnerability for the socioeconomic factors due to the presence of extended beaches (> 1 km) with several recreational and touristic activities, these areas did not get a high-risk value because the amount of oil calculated to strand on these coasts was minor compared to the volume that reached the gulf of Trieste and the Po Delta. The same consideration can be extended to the areas of Kornati and Brijuni National parks in Croatia. The areas with the highest risk level are the same for each type of oil considered. The highest risk values were recorded for crude oil and diesel oil, particularly in the 50% stranded oil scenario, but after a longer time after release compared to bunker oil. Risk assessment based on 30% and 50% of stranded oil indicated, as expected, an increase in risk with the amount of stranded oil, yet the identification of areas most at risk remained similar between the two scenarios. To give an overview of the results, some examples of the risk maps created with the single vulnerability factors are shown (Figs. 84-85). Figs. 86-91 present the final risk maps based on the 30% and 50% stranded oil, for the three oil types (bunker, crude, diesel), considering the total coastal vulnerability. As for the maps of the vulnerability factors (Section 5), risk scores were categorized in four classes and visualized in GIS with different colours: very low risk (1-2, green), low risk (3-5, yellow), medium risk (6-7, orange), high risk (8-9, red). Some areas have no risk values due to the presence of no data in the vulnerability factor maps or in the hazard maps. All maps were included in the geopackage file for QGIS already created for the vulnerability factor maps (see Section 5). Figure 84. Close up of the Gulf of Trieste, the Po delta, the Emilia-Romagna coastline, and the Kvarner Gulf, with the risk values calculated with the 50% stranded bunker oil and the socioeconomic vulnerability factors. 126 127 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Figure 86. Maps of the risk index based on the 30% stranded bunker oil (68 hours after release) and the total coastal vulnerability with a zoom on the Gulf of Trieste and the Po delta. Figure 85. Close up of the Gulf of Trieste, the Po delta, and the Kvarner Gulf, with the risk values calculated with the 50% stranded crude oil and the geomorphological vulnerability factors (coast cleaning difficulty). Figure 87. Maps of the risk index based on the 50% stranded bunker oil (131 hours after release) and the total coastal vulnerability with a zoom on the Gulf of Trieste and the Po delta. 128 129 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Figure 88. Maps of the risk index based on the 30% stranded crude oil (83 hours after release) and the total coastal vulnerability with a zoom on the Gulf of Trieste and the Po delta. Figure 90: Maps of the risk index based on the 30% stranded diesel oil (83 hours after release) and the total coastal vulnerability with a zoom on the Gulf of Trieste and the Po delta. Figure 89. Maps of the risk index based on the 50% stranded crude oil (173 hours after release) and the Figure 91. Maps of the risk index based on the 50% stranded diesel oil (185 hours after release) and the total coastal vulnerability with a zoom on the Gulf of Trieste and the Po delta. total coastal vulnerability with a zoom on the Gulf of Trieste and the Po delta. 130 131 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 7 / Conclusions The Risk Assessment is a fundamental step in each contingency planning since it allows to identify the type of risk to which different areas are exposed, and to plan for possible mitigation measures, or for intervention measures in case of need. The results of 7 NAMIRS Activity 2.1 will thus be useful in particular for NAMIRS Activity 2.3, but more in general they will contribute to the overall goals of the whole project. The Risk Assessment in NAMIRS was performed applying a multidisciplinary, holistic, and participative methodology, integrating results of marine traffic analysis, oil spill simulations, marine spatial planning, stakeholders’ involvement, literature information, and expert knowledge. While relying on established procedure in the scientific community, the methodology was adapted to the specific needs of the NAMIRS project. The results fulfilled the goals of Activity 2.1, but were also limited by the time, spatial, and financial constraints of NAMIRS. Thus, we list here some possible developments that might be explored in future projects. Marine traffic analyses are the basis for hazard estimation in case of oil spills. Neverthe-Conclusions less, they are time and resource consuming. Thus, for NAMIRS we were able to perform these analyses limiting the rigorous statistical approach to the Gulf of Trieste (anyway the busiest area in the Northern Adriatic Sea), extrapolating the results to the rest of the study area. More significant results might be obtained by performing the analyses on a longer dataset of sea currents and including a larger study area. The oil spill simulations were planned to give us a statistically significant estimation of the exposure of coastal areas to oil spills. Also in this case, extending the simulations to a longer timeframe would have increased the robustness of the results. Other possible developments include analysis per different meteorological scenarios (e.g., during ex-treme weather, considering different wind regimes), and the inclusion of additional type of oils. While we considered the most abundant oils being transported in the Northern Adriatic Sea, each oil has its own characteristics that make it more or less impacting in case of a spill, thus including more types of oil would strengthen the confidence in the results. For the mapping of the receptors potentially impacted by an oil spill we relied mostly on publicly available databases of sea and coast use. While these databases are maintained by EU infrastructures (e.g., EMODnet), not always is the information in them accurate nor updated. There are also differences in how the information is provided by different countries, thus it was not straightforward to use this information for the purposes of marine spatial planning. Among the vulnerability factor groups, the one most lacking in information is also the one that might be considered the most immediately impacting on the life of people, i.e., the socioeconomic group. Among the databases that we accessed there were almost no information on areas devoted to different type of activities, e.g., touristic activities, industrial activities, recreation, etc. This is a major drawback for a proper assessment of the risk. The information gathered might be complemented assessing 132 133 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment other, more specific databases (e.g., local regulatory plans), which are also less easily The workshops were also a good occasion for the outreach of the selected stakeholders accessible. In some cases, the use of aerial photography or remote sensing (satellite) on NAMIRS and its goals. The partners involved in Activity 2.1 plan further outreach ac-monitoring, through appropriate analysis approaches, might also improve the receptors tivities, such as participations to international conferences and publication of scientific identification and their vulnerability assessment. articles in international journals. The most obvious and ambitious development would be to enlarge the area of application of the risk assessment to the whole Adriatic Sea and possibly beyond. Disasters have no borders, and this is true in particular for disasters on sea, where the currents and winds can easily disperse the agent causing impact (e.g., pollutants, oil), thus reducing its impact, but can also spread it over huge areas, across different administrative and po-litical entities, habitats, and human and non-human populations, effectively amplifying its impact. Thus, further cross-border cooperation in this field is of the utmost importance. The result of the Environmental Risk Assessment of Activity 2.1 is a statistically based definition of the oil spill risk of coastal areas. Nevertheless, no statistical approach can be of much help during an emergency, when the field operators need to take fast and informed decisions in order to respond efficiently. Only an operative system, forced by real-time meteorological conditions, providing short-term forecasts of the fate of an oil spill can give useful information during an emergency. While the elements of such system are the same as those applied here (e.g., a high-resolution, 3D hydrodynamic model with a Lagrangian particle-tracking module able to simulate the fate of oils with different chemical and physical characteristics), the operational set-up of it exceeded the constraints of NAMIRS. Future projects should consider the possibility to build and implement a specific, real-time, possibly open and free, oil spill simulator, and to integrate it into the Standard Operating Procedures in case of incidents at sea. One of the most interesting and useful activities in Activity 2.1 was the involvement of the stakeholders. With the workshops we collected useful information that allowed us to include not only our own, necessarily limited, knowledge and expertise, in the risk assessment procedure, but also the opinion of a much higher number of persons, institutions, and companies, directly involved in the exploitation of the sea and of the coastal areas. A possible refinement of the results obtained through the stakeholders’ workshops would be to apply the Delphi method of priority selection, in order to weight stakeholders’ answers by their respective expertise and increasing the confidence in the results. Furthermore, the NAMIRS partners plan later to open up the participation to the questionnaires to a general public (possibly online or during outreach activities). In this way, the opinion of selected experts collected during the workshops will be complemented by the opinions of a wider, non-professional public. NAMIRS stakeholders’ workshops already sparked some interesting developments. The oil spill simulations set up was possible also thanks to the information on the most common oils travelling in the Northern Adriatic Sea and their characteristics provided by SIOT, a stakeholder that participated in the Italian workshop. Furthermore, another stakeholder, the Marine Protected Area of Miramare, requested OGS help for the preparation of a specific contingency plan in case of oil spill for the area under protection. Our conclusion is that any future project in this field should foresee appropriate ways of stakeholders’ involvement. 134 135 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment 8 / References Bendoni M., Laurent C., et al., 2022. Implementation of Lagrangian methods for pollutant transport and connectivity. Interreg-Med Sharemed Project Deliverable 4.6.1. 8 Birkmann J., 2007. Risk and vulnerability indicators at different scales: Applicability, usefulness and policy implications. Environmental hazards, 7(1), 20-31. Bruschi A., Lisi I., De Angelis R., Querin S., Cossarini G., Di Biagio V., Salon S., Solidoro C., Fassina D., Ancona S., Silvestri C. 2021. Indexes for the assessment of bacterial pollution in bathing waters from point sources: The northern Adriatic Sea CADEAU service, J. Environ. Manag., 293, art. no. 112878, https://doi.org/10.1016/j.jenvman.2021.112878. Burmeister et al., 2014. Assessing the Frequency and Material Consequences of Collisions with Vessels Lying at an Anchorage in Line with IALA iWrap MkII, TransNav, v.8, n.1, p.61-68. References Canu D., Zunino S. et al., 2022. Atlas of ecosystem status, pressures and environmental hazard index. Interreg-Med Sharemed Project Deliverable 4.3.1. Cardona, O. D., 2005. Indicators for Disaster Risk and Risk Management. Program for Latin America and the Caribbean: Summary Report, Manizales, Columbia: Instituto de Estudios Ambientales, Universidad Nacional de Columbia, 2005. Depellegrin, Daniel, Stefano Menegon, Giulio Farella, Michol Ghezzo, Elena Gissi, Alessandro Sarretta, Chiara Venier, and Andrea Barbanti. “Multi-Objective Spatial Tools to Inform Maritime Spatial Planning in the Adriatic Sea.” Science of The Total Environment 609 (December 31, 2017): 1627–39. https://doi.org/10.1016/j.scitotenv.2017.07.264. Falace A., Kaleb S., Curiel D., Miotti C., Galli G., Querin S., ..., Bandelj, V. 2015. Calcareous bio-concretions in the northern Adriatic Sea: habitat types, environmental factors that influence habitat distributions, and predictive modeling. PLoS One, 10(11), e0140931. Fortibuoni T, Canese S, Bortoluzzi G, et al., 2020. Bathymetry data (GeoTIFF grid format) of the northern Adriatic tegnùe di Chioggia site collected in 2014-2015 for the De-FishGear project. Integrated Earth Data Applications (IEDA). https://doi.org/10.26022/ IEDA/329815. Furlan, Elisa, Silvia Torresan, Andrea Critto, and Antonio Marcomini. “Spatially Explicit Risk Approach for Multi-Hazard Assessment and Management in Marine Environment: The Case Study of the Adriatic Sea.” Science of The Total Environment 618 (March 2018): 1008–23. https://doi.org/10.1016/j.scitotenv.2017.09.076. 136 137 North Adriatic Maritime Incident Response System D2.1 / Report on Environmental Risk Assessment Gordini E, Saul C, 2020. Bathymetry data (GeoTIFF grid format) of the northern-most Petersen, J., et al. 2019. Environmental Sensitivity Index Guidelines, Version 4.0. NOAA Adriatic area: Interreg Ita-Slo 2007-2013 TRECORALA Project. Integrated Earth Data Ap-Technical Memorandum NOS OR&R 52. plications (IEDA). https://doi.org//10.26022/IEDA/329903. Ponti M., 2020. Single-beam bathymetry data (ESRI ASCII grid format) of selected meso-Gucma et al., 2021. Reconstructing maritime incidents and accidents using causal mod-photic biogenic reefs from the northern Adriatic Sea. Integrated Earth Data Applications els for safety improvement: based on a case study. J Mar Sci Eng, no. 12, 1414, p. 14. (IEDA). doi: 10.26022/IEDA/329803. Halpern, B. S., S. Walbridge, K. A. Selkoe, C. V. Kappel, F. Micheli, C. D’Agrosa, J. F. Bruno, Querin S., Bensi M., Cardin V., Solidoro C., Bacer S., Mariotti L., Stel F., Malačič V., 2016. et al. “A Global Map of Human Impact on Marine Ecosystems.” Science 319, no. 5865 Saw‐tooth modulation of the deep‐water thermohaline properties in the southern Adriatic (February 15, 2008): 948–52. https://doi.org/10.1126/science.1149345. Sea. J. Geophys. Res. Oceans 121, 4585–4600. https://doi.org/10.1002/2015JC011522. IALA, 2022. Guideline G1124 The Use of Ports and Waterways Safety Assessment (PAW-Silvestri C., Bruschi A., Calace N., Cossarini G., De Angelis R., Di Biagio V., Giua N., Peleggi SA) MkII Too. M., Querin S., Saccomandi F., Salon S., Solidoro C., Spada E. CADEAU project - final report (2020) Figshare, https://doi.org/10.6084/m9.figshare.12666905.v1. Jokuty, Paula, et al., Environment and Climate Change Canada, A Catalogue of Crude Oil and Oil Product Properties (1999)- Revised 2022, Report EE-165, Environment and UNDRO, 1979: “Natural Disasters and Vulnerability Analysis”: Report of Expert Group Climate Change Canada, 2022. Meeting, 9–12 July 1979. Landis, Wayne G. “Ecological Risk Assessment Conceptual Model Formulation for Non-UNISDR, 2005. Hyogo framework for action 2005–2015: Building the resilience of na-indigenous Species.” Risk Analysis 24, no. 4 (2004): 847–58. https://doi.org/10.1111/ tions and communities to disasters. In Extract from the final report of the World Con-j.0272-4332.2004.00483.x. ference on Disaster Reduction (A/CONF. 206/6) (Vol. 380). Geneva: The United Nations International Strategy for Disaster Reduction. Laurent C., Querin S., Solidoro C., Melaku Canu D., 2020. Modelling marine particle dynamics with LTRANS-Zlev: Implementation and validation. Environmental Modelling & Software, 125, 104621. https://doi.org/10.1016/j.envsoft.2020.104621. Marshall J. C., Adcroft A., Hil C., Perelman L., Heisey C.,1997. A finite-volume, incom-pressible Navier-Stokes model for the studies of the ocean on parallel computers. Journal of Geophysical Research, 102, 5753–5766. Melaku Canu D., Solidoro C., Bandelj V., Quattrocchi G., Sorgente R., Olita A., Fazioli L., Cucco A. 2015. Assessment of oil slick hazard and risk at vulnerable coastal sites. Marine Pollution Bulletin. Vol., 94, issue 1-2, 15 May 2015, Pages 84-95. Menegon, Stefano, Daniel Depellegrin, Giulio Farella, Alessandro Sarretta, Chiara Venier, and Andrea Barbanti. “Addressing Cumulative Effects, Maritime Conflicts and Ecosystem Services Threats through MSP-Oriented Geospatial Webtools.” Ocean & Coastal Management 163 (September 2018): 417–36. https://doi.org/10.1016/j.oce-coaman.2018.07.009. Menoni S, Molinari D, Parker D, Ballio F, Tappsel S, 2012. Assessing multifaceted vulnerability and resilience in order to design risk mitigation strategies. Nat Hazards:1–26. https:// doi. org/ 10. 1007/ s11069- 012- 0134-4. 138 139 Kataložni zapis o publikaciji (CIP) pripravili v Narodni in univerzitetni knjižnici v Ljubljani COBISS.SI-ID 181850115 ISBN 978-961-7041-14-9 (Fakulteta za pomorstvo in promet, PDF)