Ship Route Oil Spill Modeling: A Case Study of the Northeast Brazil Event, 2019

Abstract

In this study, we investigate the circulation and chemical processes associated with the deposition of the largest oil spill that reached the northeast coast of Brazil during the second half of 2019. Using the Oil Spill Contingency And Response model (OSCAR), we performed both deterministic and probabilistic simulations of oil spills from tanker ships that were present in the sea in the region at the time. The study used a dataset comprising the latitudinal distribution of oil sightings along the coast between 31 August and 2 December 2019 (box plot analysis) provided by the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). The total amount of oil that reached the coast during this period (approximately 5000 tons) and the date and location of the first sighting (30 August, in the southern part of the state of Paraíba (PB)) were also used as parameters to assess the results of the 31 simulations conducted for ships en route near the area of interest between July and August 2019. The results indicate that a leak having occurred through a mobile source is the most plausible hypothesis for explaining the observed temporal–spatial arrival of the oil leaks along the Brazilian coastline. We suggest that prevention, monitoring, and international cooperation are essential for reducing the risks of future environmental accidents of the kind analyzed in this study and to protect the environment and communities affected.

1. Introduction

The oil industry poses the highest risk of environmental accidents, with the likelihood of spills occurring at every stage of its operations, from drilling to distribution [1,2]. The increasing globalization and demand for fossil fuels have led to a rise in the maritime transportation of oil and its derivatives [3]. After significant fluctuations in volume transported before the 1990s, stabilization occurred in the following years, and the average oil trade volume was around 10,000 ton-miles per year. Crude oil seaborne trade has a significant share of the total world seaborne trade. Currently, tanker movements comprise approximately 80% crude oil and about 20% oil product [4].

While the increase in maritime transportation might suggest a greater probability of environmental risks, a decreasing trend in the frequency of maritime oil spills continues despite growing global demand. The demand for oil has increased tanker traffic between countries around the world, which increases the risks of both accidental and intentional maritime oil spills. The maritime transportation of crude oil has expanded since the early 19th century to meet the growing demand of the global market. Currently, over 90% of oil transportation around the world is carried out through tanker ships [5]. It is estimated that oil spills from tankers constitute approximately 13% of all oceanic oil pollution [6].

It is crucial, therefore, to look into the characteristics of these tanker ships in case they are involved in oil spills accidents. Oil-carrying ships are divided into five main categories according to their tank volume: Panamax (60,000 to 79,999 dwt), Aframax (80,000 to 119,999 dwt), Suezmax (120,000 to 199,999 dwt), VLCC, and ULCC (200,000 dwt and above). The shipping routes of tanker ships are recorded and well established, and the navigation data provided by the Automatic Identification System (AIS) allow for their immediate identification. AIS is an international maritime security communication system that uses ship tracking equipment to monitor the activities of cargo ships worldwide [7]. AIS allows for the identification of potential causes of oil discharge at sea, the evaluation of ship routes, and their distances from sensitive environmental areas.

Despite technological advancements to protect oil transportation operations, including improving safety conditions, implementing sophisticated monitoring, and accident prevention by authorities [8], maritime oil transportation still presents high risks to the environment and society [9]. Nevertheless, the total number of maritime oil spill accidents has decreased in the last 50 years, likely due to increased attention from government agencies around the world to oil spill accidents and improvements in communication and technology related to oil transportation [4].

While large ships optimize oil transportation, their accidents pose serious spill risks. The basis of risk-based ship design has been highlighted in numerous studies [10,11,12,13]. It is important to consider a ship’s deadweight tonnage (DWT) to identify mobile oil leak sources. This can be achieved by linking AIS data and taking into account weathering processes to determine the quantity of oil reaching the coastline. This approach integrates the formal safety analysis (FSA) of the International Maritime Organization (IMO) and stresses the need to explicitly consider risks in ship design for safer and more sustainable maritime transport [14,15,16,17]. In one study [13], a qualitative analysis revealed a spilled volume ranging from 5000 to 45,000 m${}^3$ of crude oil, which aligns with the order of magnitude of oily residues found on the Brazilian coast in the event of 2019 (approximately 5000 tons).

Oil spills in marine and coastal environments represent a serious and damaging socio-environmental threat. The release of oil and its derivatives into the oceans can have significant effects on economic activities and the environment, both in acute and chronic spills. An oil spill at sea can adversely affect organisms, ecosystems, and coastal activities (e.g., beach bathing, diving, fishing) and can lead to concerns from the population, local businesses (e.g., hotels, restaurants, and tourism), the local government, industries that rely on marine resources, and other sectors of society that utilize the affected environment [18,19,20,21]. Since the 1940s, the oceans have experienced 25 major oil spill events that have destroyed wildlife, affected various regions, and impacted tourism and recreational activities [22,23].

A powerful tool available to the scientific community to reconcile the balance between exploration and preservation is oil spill modeling, which is already in use by the scientific community. These models range from simple trajectory determinations or particle monitoring to three-dimensional oil trajectory models that include response actions and biological processes, among others [24]. Oil spill modeling is a fundamental tool in the study and management of E&P activities. This technique can define the indirect influence area of oil activities, which forms the basis for the holistic environmental diagnosis. Modeling can also be used to define scenarios via simulations, thereby enabling the development of necessary strategies for emergency responses to maritime oil spill accidents within the context of individual emergency plans [25].

In this context, the objective of this study was to perform dispersion modeling of oil that was hypothetically released into the sea by tanker ships along the east and northeast coasts of Brazil with the goal of increasing our understanding of the processes involved in the arrival of oil on the country’s coastline during the second half of 2019. To the best of our knowledge, this work is the first to perform oil spill modeling along a ship route in the Northeast Brazil 2019 oil spill event. We hope that this approach will be embraced and implemented with greater frequency in the future. This oil spill event occurred in late August 2019 when the northeastern coast of Brazil was hit by approximately 5000 tons of oil residue, which, by the end of December, had contaminated approximately 3000 km of coastline, including protected areas and coral reefs [20,26,27,28,29]. During the oil spill period or prior to that period, no accidents were reported by oil companies and tankers or platforms in the region. After additional subsequent analysis, the oil was found to have Venezuelan biomarkers [29,30,31,32,33].

2. Materials and Methods

The ship selection used in the oil spill simulations was based on the types of vessels that could have been transporting oil along the northeast coast of Brazil between April and August 2019. The selection of ships was based on their classification according to the type of cargo they carried, i.e., oil/chemical tanker, crude oil tanker, oil products tanker, shuttle tanker, chemical tanker, tanker, and bunkering tanker. During the selected period, 964 ships were identified in the Brazilian jurisdictional waters via the AIS data provided by the Brazilian Navy. The positions of the ships were filtered for those that were navigating within a region of interest. This region was defined as the polygon found in the study by [34] plus a surrounding area of up to 2 degrees (black polygon in Figure 1), which was identified as the most likely region of origin for the oil spill. Considering this delimited region and the same period, 200 ships were selected along 225 individual routes, with speeds ranging from 10 to 15 knots as they traversed two main maritime routes: northwest/southeast (approximately 135${}^{°}$) and northeast/southwest (approximately 220${}^{°}$). The ships comprised 31 Aframax (15.5%), 22 Panamax (11%), 26 Suezmax (13%), and 34 VLCC (17%), while the remaining 87 (43.5%) were small ships (i.e., smaller than Aframax) whose transported volume was likely to be insufficient to cause the environmental impact of 2019. Thus, the four selected types of oil tankers were the most representative vessels that crossed the region of interest within the specified period and were consequently utilized in the oil dispersion simulations.

The total number of ships used in the simulation was further reduced to 31, comprising only those that navigated within the region of interest between July and August 2019. This remaining group of ships included Panamax (7), Aframax (9), Suezmax (6), and VLCC (9) vessels (colored routes in Figure 1). This time period was chosen as the most likely timeframe during which the oil spill occurred at sea based on the distance of the routes from the coast and the mean speed of ocean currents in the region. The approximate dimensions of these vessels used for oil transportation were provided in [16] (

Table 1. Dimensional parameters of the ships analyzed in oil spill simulations. Adapted from [16].

Classification	Volume of Liquid Cargo (m3)	Cargo Tank Configuration	Average Tank Size
Panamax	79,214	6 × 2	5611 t (6601 m${}^3$)
Aframax	122,779	6 × 2	8697 t (10,232 m${}^3$)
Suezmax	171,009	6 × 2	12,113 t (14,251 m${}^3$)
VLCC	340,015	5 × 3	19,268 t (22,668 m${}^3$)

).

The total volume of oil used in the dispersion simulations considered the size of these vessels; however, without exact knowledge of the volume spilled into the sea, estimating the total spill volume could result in overestimation in the simulation results. It is known that oil production incurs significant costs, from exploration and transportation to refining and distribution; therefore, the scenario of a ship intentionally spilling its entire cargo into the sea is highly unlikely. Assuming that only a fraction of the total oil quantity was spilled into the sea, [13] estimated that a a plausible hypothesis is that the spill resulted from an accidental rupture of one or two internal tanks of these vessels, which would not compromise navigation safety. In the case of an intentional spill, the authors suggest that such a release could have been intentional to ensure safety, and the release rate would be consistent with the natural pumping of oil during the loading and unloading processes. For the dispersion simulations, the volume of two internal tanks and an average oil pumping rate of 4000 m${}^3$ h${}^{-1}$ [35] were considered (N.B. this value can vary from 2000 to 6000 m${}^3$ h${}^{-1}$, depending on the type of vessel and its operational characteristics [36]). By considering the leakage from two individual internal tanks, it was possible to estimate the duration of the spill for each type of vessel used in the study. This information is presented in

Table 2. Simulation parameters used in oil dispersion modeling.

Classification	Volume of Two Tanks (m3)	Percent of the Total Capacity (%)	Oil Spill Duration (h)
Panamax	13,202	16.7	3.3
Aframax	20,464	16.7	5.1
Suezmax	28,502	16.7	7.1
VLCC	45,336	13.3	11.3

.

2.1. Oil Spill Model

The OSCAR (Oil Spill Contingency And Response) model system, as detailed by [37,38], serves as a tool for the objective analysis of alternative spill response strategies to contingency, offering valuable insight into hydrocarbon transport, oil fate, and effects during a release and spreading, enabling the simulation of different response strategies and configurations.

Key features of OSCAR include three-dimensional modeling of oil in the water column, representation of oil by 25 pseudo-components, an extensive oil database with experimental weathering data, stochastic simulations, consideration of biological effects on marine organisms, and the interaction of oil with various shoreline types. Its applications encompass oil spill risk assessment, response planning, and the modeling of oil spill response operations. The model comprehensively considers weathering, as well as the physical, biological, and chemical processes affecting oil at sea, with a strong connection to laboratory activities at SINTEF focusing on oil weathering.

The model conceptualizes oil as particles influenced by currents, winds, and turbulent diffusion, undergoing weathering processes such as evaporation, dissolution, and dispersion, governed by process such as surface spreading, advection, entrainment, emulsification, and volatilization to determine transport and fate of oil at the surface. In the water column, horizontal and vertical advection and dispersion of entrained and dissolved hydrocarbons are simulated using random walk procedures. Vertical turbulence is influenced by wind speed (wave height) and depth.

Wave height and period calculations derive from 10 m wind data using the JONSWAP spectrum, with fetch limited to 100 km. The model primarily represents local wind-driven waves, important for entrainment and mixing processes. OSCAR employs a stochastic differential equation to simulate transport, representing pollutants as particles sampled at the release location. Wave height is calculated as:

$$H_s=MIN(h_{max},h_{nodim})u^2/g$$

(1)

where

$$h_{nodim}=h_c\sqrt{gf/u^2}$$

(2)

With constant $h_{max}$ = 0.243, $h_c$ = 0.0016, and g = 9.81 m s${}^{-2}$. Fetch is calculated considering the presence of land and ice as in [39].

As referenced in the technical description of the OSCAR model, the transport is simulated as the superposition of a mean local velocity plus a random turbulent component. This is analogous to the advection–diffusion equation, where, in this case, the diffusive step is turbulent diffusion. To achieve transport, pollutants are represented through particles that are sampled at the release location. These particles represent individual solutions of a stochastic differential equation:

$$dX\left(t\right)=(u+{\delta }_x{K}_x\left(x\right))dt+\sqrt{6K_x\left(x\right)}dW\left(t\right)$$

(3)

where u is current velocity, $K_x$ is diffusivity, and x represents any of the three spatial dimensions. The first term in the equation represents the advective step and the second term the turbulent step. By default, the model does not calculate the derivative of $K_x$, so the equation that is effectively solved is:

$$dX\left(t\right)=udt+\sqrt{6K_x\left(x\right)}dW\left(t\right),$$

(4)

which corresponds to an advection–diffusion equation with constant diffusivity. The horizontal diffusivity coefficient $K_x$ is calculated from data on dye dispersion [40] as reviewed by [41]:

$$K_x=0.0027t^{1.34}$$

(5)

As the variance of a pollutant cloud grows, the cloud is dispersed by turbulence associated with increasingly higher spatial scales, such that the apparent diffusion coefficient increases with time. A maximum value for $K_x$ is set to be 100 m${}^2$ day${}^{-1}$ based on [42]. The vertical turbulent diffusion coefficient above is related to the wave conditions following [43]:

$$K_z=0.028\frac{H^2}{T}{e}^{-2kz}$$

(6)

where H is wave height, T is wave period, and k is the wave number. The minimum $K_z$ value is set to 10${}^{-4}$ m${}^2$ s${}^{-1}$ [42], corresponding to a low mixing zone below the pycnocline.

2.2. Model Setting

In this study, we used 2000 Lagrangian particles and Bunker-type oil with an API gravity of 14 and a viscosity of 28,000 cP, representing heavy and highly viscous oil, which was the same type of oil used in [34]. These oil characteristics were chosen based on chemical studies on the oil’s origin conducted by IEAPM, indicating that the oil had geochemical characteristics very similar to some Venezuelan crudes [19,32,44]. The particles were driven by daily hydrodynamic data provided by the Copernicus Marine Environment Monitoring Service (CMEMS) [45] with a spatial resolution of approximately 10 km and hourly atmospheric data provided by ERA5 with a spatial resolution of approximately 25 km. The data on the annual average water column temperature and salinity up to 100 m depth are from in situ measurements obtained by the PIRATA moored buoy array [46] at 8${}^{°}$ S, 30${}^{°}$ W.

The starting coordinates for the oil dispersion simulations were selected based on AIS data emitted by the ships along their routes, choosing the location closest to the area of interest. The modeling parameters are summarized in

Table 3. Model setting.

Parameter	Value
Number of liquid/solid particles	2000
X direction resolution (m)	2980
Y direction resolution (m)	2900
Z direction layers	10
Maximum concentration grid depth (m)	1000
Oil type	Heavy bunker
${}^{°}$ API oil	14
Viscosity (cP)	28,000

. Based on identification of the route closest to the total amount of oil on the coast and the spatio-temporal identification of the first contact, we conducted a probabilistic study of this scenario via 500 simulations with the same initial characteristics and forcing as the selected deterministic simulation. Our aim was to aggregate and identify the uncertainties in each simulation resulting from turbulent and advective movements.

2.3. Model Evaluation

To evaluate the model outputs, we used a dataset comprising the latitudinal distribution of oil sightings along the coast between 31 August and 2 December 2019 (box plot analysis) provided by the Brazilian Institute of Environment and Renewable Natural Resources (IBAMA). The total amount of oil that reached the coast during this period (approximately 5000 tons), the latitudinal distribution observed along the Brazilian coast, and the date and location of the first oil sighting (30 August in the southern part of the state of Paraíba (PB), around 7${}^{°}$33${}^{\prime }$ S and 34${}^{°}$49${}^{\prime }$ W) were also used as parameters to assess the results of the 31 simulations conducted for ships en route near the area of interest between July and August 2019.

To incorporate an additional experiment featuring observational data and well-known initial conditions (e.g., oil type, spill location, total spilled volume, duration, and date of the spill) to assess the representativeness of the OSCAR results, we used the case study of the floating production unit (FPU) P-53 located 120 km off the coast of the State of Rio de Janeiro (RJ). The initial conditions used for the simulation were obtained from the Accident Investigation Report made available by IBAMA, and the summary settings can be found in

Table 4. Model setting.

Parameter	Value
Number of liquid/solid particles	2000
X direction resolution (m)	2980
Y direction resolution (m)	2900
Z direction layers	10
Maximum concentration grid depth (m)	1000
Latitude	22.424${}^{°}$ S
Longitude	39.957${}^{°}$ W
Oil type	Blended Oil
${}^{°}$ API oil	22.8
Viscosity (cP)	28,000
Start date	2 March 2019 at 8 p.m.
Duration of spillage	12 h
Volume	122 m${}^3$
Simulation period	12 days

. The meteo-oceanographic forcings were the same as those used in the oil tanker experiments (CMEMS and ERA5). We selected this well-documented case study due to the availability of sufficient data and information that could be utilized in the oil dispersion model and the assessment of results. In the future, additional case studies using meteo-oceanographic forcings with higher resolution could be used to enhance the evaluation of the OSCAR simulation results within Brazilian jurisdictional waters.

The evaluation of the model results was based on the observation of the arrival of oil from the P-53 platform in the northern coastal region of Rio de Janeiro, near the beaches of Arraial do Cabo, Búzios, and Cabo Frio, around 22.875${}^{°}$ S and 41.964${}^{°}$ W between 3 and 4 April 2019, approximately 10 to 11 days after the onset of the spill. It is noteworthy that the horizontal resolution of CMEMS is considered low for the analysis of dynamic oceanographic processes in shallow waters over the inner continental shelf, especially on a scale of hundreds of meters to a few kilometers between one beach and another; however, it allows for a general analysis of the spatiotemporal representativeness of the oil’s arrival. The results are shown in Figure 2.

The model results indicated that the oil reached the coast on April 1 in the same region where it was actually observed, i.e., near the beaches of Arraial do Cabo (RJ), approximately two days prior to the observation. The analysis demonstrated that the ocean circulation system over the continental shelf, driven by a change in wind direction between 30 March and 1 April (see the stick plot in the lower panel), played a significant role in the fate of the surface oil by carrying it toward the coast. Thus, considering the limitation in the resolution of the applied forcings, the model was able to represent a realistic case study that occurred in 2019.

3. Results

Figure 3 displays the latitudinal distribution of oil arrival on the coast at the end of the simulation in each of the 31 experiments representing spillage from ships en route. These experiments consider the volume of two internal tanks and the duration of emptying based on an oil pumping rate of 4000 m${}^3$ h${}^{-1}$.

The latitudinal distribution of oil arrival on the coast showed that none of the VLCC-type vessels (experiments 23 to 31) exhibited a spatial pattern like the one observed, where the maximum (75th) and minimum (25th) percentiles did not coincide with the oil arrival according to IBAMA data, presenting a predominant dispersion toward the northern coast; thus, in these scenarios, the major concentration of oil touch occurred between 12${}^{°}$ S and 5${}^{°}$ S, unlike what was observed (between 16${}^{°}$ S and 10${}^{°}$ S). However, some experiments showed this similarity between the percentiles and the distribution deviation, e.g., experiments 2 (Panamax), 9 and 11 (Aframax), and 17 and 21 (Suezmax). Therefore, we chose these experiments for more detailed analysis.

The results of the minimum oil arrival time on the coast, the date of arrival, and the maximum quantity of oil on the coast in each experiment are shown in

Table 5. Results of the minimum oil arrival time on the coast, the date of oil arrival, and the maximum quantity of oil on the coast in each experiment. The experiments highlighted in bold show similarity in the latitudinal distribution of oil arrival on the coast.

Experiment	Start Date of Experiments (day/month)	Minimum Arrival Time (days)	Arrival Date on the Coast (day/month)	Maximum Oil on the Coast (tons)
1	09/07	54	01/09	34.2
2	12/07	18	30/07	593.8
3	12/07	76	26/09	4.5
4	13/07	18	31/07	910.4
5	06/08	9	15/08	3341.3
6	07/08	12	19/08	2818.5
7	08/08	14	22/08	1175.6
8	06/07	22	28/07	1450.8
9	17/07	27	13/08	220.4
10	18/07	14	01/08	1190.4
11	22/07	15	06/08	2410.4
12	29/07	31	29/08	97
13	31/07	42	11/09	81.1
14	02/08	52	23/09	12
15	03/08	19	22/08	359.4
16	16/08	6	22/08	5834.3
17	08/08	13	21/08	7326.2
18	04/08	117	29/11	2.1
19	02/08	49	20/09	123.6
20	27/07	No arrival	No arrival	-
21	02/07	17	19/07	1004.3
22	11/08	18	29/08	1458.5
23	09/07	25	03/08	757.3
24	13/07	31	13/08	533
25	19/07	50	07/09	210.1
26	19/07	25	13/08	515
27	21/07	32	22/08	230.7
28	27/07	37	02/09	200.8
29	28/07	25	22/08	433.4
30	05/08	28	02/09	402.7
31	17/08	30	16/09	464.9

. As highlighted, the experiments conducted on the VLCC ship routes do not resemble the oil arrival on the coast, dispersing mainly toward the northern coast, and they have a low total oil arrival quantity (average of 416.4 tons), which is inconsistent with the observed data. The experiments highlighted with similarity in the latitudinal distribution of arrival on the coast (2, 9, 11, 17, and 21, bold in Table 5) do not correspond to the total quantity, location, and arrival date from the IBAMA observations, demonstrating that the analysis of the latitudinal distribution of oil arrival on the coast in isolation cannot serve as a reference for choosing an experiment that more faithfully represents the actual event.

Additionally, we also sought correspondence between the total oil arrival on the coast (approximately 5000 tons) and a temporal window of the first arrival near the end of August and the beginning of September in the region south of the state of Paraíba.

Experiment 22 (dashed green line in Figure 1) stood out as the only one with a high total quantity of oil on the coast (1458.5 tons) and the first touch day between late August and early September (29/08), although the latitudinal oil distribution is not consistent. In this experiment, the latitude of the first touch on the coast was 7${}^{°}$25${}^{\prime }$ S, consistent with the latitude of the first sighting location (7${}^{°}$33${}^{\prime }$ S); however, the total quantity on the coast does not match the oil collected in situ in early December 2019. In this experiment, the considered vessel was of the Suezmax type, and the total volume spilled was increased to three internal tanks (42,753 m${}^3$), resulting in a maximum total quantity of oil on the coast of 4686.1 tons, a value closer to the observed data, but without a significant increase in the latitudinal oil distribution.

Figure 4 displays the outcome of experiment 22 regarding the total oil concentration in the water column (<100 m) on 29 August and its arrival on the coast (cyan dots), as well as the main dispersion directions to the north and south and the coastal arrival locations after the simulation period on 2 December 2019 (black dots). The behavior of the oil and weathering processes is highlighted in Figure 5. It was observed that, after the sixth day, the oil exhibited a higher concentration in the water column than on the surface, indicating a sinking process possibly related to the physicochemical transformation of the oil in seawater, while coastal arrival and oil sedimentation began on the seventeenth day. Importantly, the estimated biodegradation process in the model showed growth of up to approximately 60%, and evaporation did not exceed 20% of the total spilled volume.

The probabilistic analysis of surface oil occurrence conducted based on experiment 22 is shown in Figure 6. The results revealed a low probability (between 5 and 10%) of oil reaching the northern coast of the State of Rio de Janeiro (RJ), which is the southernmost point of oil observation along the coast and was not found in the deterministic experiment. Nevertheless, the model was able to demonstrate that, albeit low, the probability of oil arrival on the coast in this region was possible, as the observation revealed. The study indicated that the coastline between the states of Ceará (CE) and the northern coast of Bahia (BA) exhibited high susceptibility to oil presence (>50%) in this scenario, and that the possibility of oil arrival extended along the entire coastline, where stains and pellets were identified.

4. Discussion

Numerical experiments designed to help to understand oil dispersion at sea have confirmed that a leak in a mobile source is the most plausible explanation for the environmental disaster on the northeastern and part of southeastern coasts of Brazil in the second half of 2019, particularly in light of the total volume reaching the shore and the latitudinal distribution. A tanker ship crossing a zonal system of surface currents transversely would result in a greater spreading of suspended particles compared to a fixed spill source within the same time frame. This hypothesis is also supported by [13], which explored non-floating oil (subsurface dispersion) originating from a moving vessel. Another study [34] also demonstrated that the oil, which was not identified in satellite imagery, likely dispersed to the subsurface after a period of approximately 7 days.

The current study was able to emphasize the importance of conducting oil dispersion experiments on mobile spill sources rather than the more common type of studies that focuses on fixed sources (e.g., natural seeps and oil exploration platforms). The environmental and socioeconomic risks of spills originating from mobile sources are much greater. The probability of the broad spreading and advection of oil from a mobile source via distinct surface current systems is higher, allowing the oil to reach a greater diversity of coastal environments and consequently exacerbating the associated impacts.

The methodology and results developed herein to better understand this specific event are applicable to other events that may occur in the future, such as possible cases of ships deliberately discharging oil into international waters for various reasons, tank cleaning, and navigation safety, among others. Many of these offshore activities, which are carried out by ships along their navigation routes, result in orphan oil spills with no identified origin. The oil dispersion modeling of mobile sources, combined with the reverse modeling of surface particles, satellite images (when available), high-resolution spatiotemporal meteoceanographic forcings, and the use of unmanned autonomous vehicles, will enable an increased surveillance of Brazilian jurisdictional waters and more precise identification of polluters.

Although the analysis of surface circulation off the Brazilian coast was not the focus of this study, the distribution of oil arrival along the coast observed in experiment 22 is consistent with the expected pattern of surface circulation for the region. The annual mean bifurcation of the southern branch of the South Equatorial Current (0–100 m) near the Brazilian continental margin occurs between 10${}^{°}$ S and 14${}^{°}$ S, but seasonal variability shows that, in the winter months (July to August), the bifurcation occurs between 17${}^{°}$ S and 16${}^{°}$ S [47], reaching the southernmost latitude in July and the northernmost latitude in November. Thus, oil dispersion on the sea surface from a transversely mobile source to the bifurcation system would result in greater north–south spreading. Additionally, mesoscale activities already identified in the region [48,49] might be capable of trapping oil patches and advecting them to more distant regions.

As also reported by [34], the physicochemical characteristics of the oil, primarily based on its density and viscosity, resulted in subsurface sinking approximately one week after the spill, as demonstrated by the inversion in the percentage of oil (%) present on the surface and in the subsurface (Figure 5). Indeed, such behavior is expected not only due to the oil’s characteristics but also because of weathering processes, especially emulsification, which can triple the volume of spilled oil and increase its viscosity by over a thousand times [50]. The chemical transformations of oil must be thoroughly understood to gain a more precise understanding of its chemical behavior in the sea, and processes such as evaporation, spreading, dissolution, and emulsification represent the primary transformation processes on a smaller time scale. A physicochemical understanding of transformation allows for an assessment of the possibility and spatio-temporal scale of oil sinking to the subsurface, which hinders environmental cleanup, as well as the surface area coverage of oil in the sea. This combined information is crucial for emergency teams dedicated to oil recovery at sea and for onshore teams responsible for cleaning up any material that reaches the coast.

Probabilistic spill scenarios are useful for emergency teams as they consider different spill conditions within a single deterministic scenario, such as the oil release rate and the initial simulation date, with the aim of encompassing different simulation conditions within that scenario and identifying the probabilities of oil presence in certain compartments (e.g., surface or shoreline) and regions. The use of probabilistic simulations is also beneficial in cases where the initial spill conditions are unknown [51]. This approach was employed by [13] at different points near the Brazilian coast. The results for Location D are particularly notable, as this location is considered the western limit of the possible spill source area and encompasses the starting point of the probabilistic simulation of the mobile source in experiment 22 (12${}^{°}$ S; 31.8${}^{°}$ W) in a northeast direction, located in the northwest portion of the polygon identified by [34]. Considering the non-floating oil drift, [13] found an intermediate level of confidence for Location D and reported that, within the delimited area, the chosen point (11${}^{°}$ S, 33${}^{°}$ W) is too close to the coast to be representative of the event. However, experiment 22 has its initial spill point further south and offshore, lasting 7.1 h and ending at 11.3${}^{°}$ S and 31.5${}^{°}$ W, being farther away from the coast and potentially reaching the shoreline by the end of August.

The probabilistic simulation of experiment 22 demonstrated extensive oil spreading toward the northern and southern coasts (Figure 6), with a higher probability of oil reaching the coast (>50%) between 6${}^{°}$ S and 12${}^{°}$ S. In this simulation, the oil spread could reach the northern region of Rio de Janeiro State (RJ) (at approximately 22${}^{°}$ S), albeit with a lower probability (0–10%). The arrival occurred on the southern coast of Paraíba State (PB) 17 days after the beginning of the spill, which is consistent with the observations according to IBAMA data. Despite these results, it is known that there are many gaps within the study, with one of the most significant being the resolution of the meteo-oceanographic forcings. To accurately combine the spatiotemporal arrival of oil on the coast, forcings with kilometer-scale spatial resolutions are not the most appropriate; therefore, future studies should improve this resolution to the scale of hundreds or tens of meters to gain a more precise understanding of the coastal environments affected by a spill.

5. Conclusions

The objective of this study was to model the dispersion of oil hypothetically released into the sea by tanker ships along the east and northeast coasts of Brazil. These studies should contribute to our understanding of the processes involved in the arrival of oil on the country’s coastline during the second half of 2019. Using AIS information, we identified a total of 31 vessels comprising four main categories of oil tankers (i.e., Aframax, Panamax, Suezmax, and VLCC) that were en route within the region of interest. These routes were selected to be simulated with the oil dispersion model.

Our results suggest that the spillage likely originated from a mobile source crossing a surface current system capable of facilitating extensive oil spreading. Notably, the region near the northeastern coast of Brazil, between 10${}^{°}$ S and 13${}^{°}$ S and 30${}^{°}$ W and 33${}^{°}$ W, was highlighted as a region where surface currents bifurcate (particularly during the months of June to August), making this the most plausible region to be reached by long-distance oil spreading.

Our study emphasizes that the northeast/southwest (approximately 220${}^{°}$) route of oil tankers deserves further investigation in future studies on oil spill modeling, orphan oil slick identification, and potential polluters. Our results demonstrate that the coastal region in this route, especially represented by experiment 22 (Suezmax-type ship), had the highest probability of being the origin of the spillage.

While this information is potentially valuable, it is important to emphasize that oil spill modeling is only a tool that should be complemented with other information and evidence (e.g., data from satellite surveillance, drones, and autonomous investigation vehicles) for a more comprehensive and accurate analysis of scenarios. Furthermore, preventative actions, continuous monitoring, and international cooperation are essential for further reducing the risks of environmental accidents and to protect the environment and communities affected by maritime oil transportation.