DEVELOPMENT OF AN INTEGRATED SYSTEM DYNAMICS ESTIMATION AND SCENARIO-BASED DECISION-MAKING FRAMEWORK FOR OFFSHORE OIL SPILL WASTE MANAGEMENT by Seyed Ashkan Hosseinipooya B.Sc., Yazd University, 2013 M.Sc., Isfahan University of Technology, 2017 THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NATURAL RESOURCES AND ENVIRONMENTAL STUDIES (ENVIRONMENTAL SCIENCE) UNIVERSITY OF NORTHERN BRITISH COLUMBIA November 2022 © Seyed Ashkan Hosseinipooya, 2022 ABSTRACT Managing the waste generated after response operations is the most challenging part of an offshore oil spill. A waste estimation is required before deciding on the transportation, treatment, and disposal of each type of oil spill waste. So, firstly, this thesis developed a system dynamics model to estimate the quantity of each type of oily waste generated after oil spill response operations, considering different aspects (e.g., weather conditions, the spilled oil volume and characteristics, response time and equipment). The results of the model for an actual oil spill in BC, Canada (2016), as the case study, showed a 86% average accuracy. Sensitivity analysis of the case study illustrated that a five-hour decrease in the response arrival time could increase the oil recovery by 26%. Moreover, sensitivity analysis showed a possibility of 45% overuse of sorbents for the case study. Response surface methodology (RSM) also was conducted, and the significant interaction effects between sea temperature and response arrival time on recovered oil and between sorbent boom weight and sorbent booms usage rate on solid waste were demonstrated. In addition to the oil spill response waste (OSRW) quantity estimation model, the study developed a scenariobased decision-making framework as the second objective to provide the most monetary beneficial strategies to deal with each collected OSRW under different scenarios of impact factors (e.g., waste quantity, waste quality, location, capacity, and availability of treatment and disposal facilities). An optimization model with an objective of minimizing net costs was developed to evaluate all scenarios using hypothetical and actual data. Results were categorized to develop the decisionmaking framework. It was illustrated that oil processing is the best option for managing liquid oily waste from spilled refined oil. For liquid oily waste from crude spilled oil, the oil refinery is the best option if the quantity is above a defined limit in this study. For solid oily waste management, i pyrolysis is the most appropriate destination. The optimum solutions and sensitivity analysis for the actual data of a case study validated the results. ii CO-AUTHORSHIP I am the principal author for all chapters of this thesis, including the design of studies and research objectives, investigation, methodology, data collection, modeling development, software, validation, writing, visualization, and project administration. I wrote the manuscripts and was responsible for the requested revisions. Dr. Jianbing Li supervised my research and contributed to the investigation and revision of the manuscripts. Dr. Guangji Hu also contributed to the review and revision of the manuscripts. Dr. Jianbing Li and Dr. Guangji Hu are included in authorship for all resulting publications. Kenneth Lee, Dr. Kelvin Tsun Wai Ng, and Hoang Lan Vu are included in the authorship of one of the publications as they contributed to the manuscript's review and revision. Publications and authorships from this thesis (published or prepared for submission): Hosseinipooya, S. A., Hu, G., Lee, K., Li, J., Ng, K. T. W., & Vu, H. L. (2022). A system dynamics modeling approach for estimation of oily waste generation from marine oil spill response: A case study of an oil spill in Central Coast of British Columbia. Frontiers in Environmental Science, 801 (chapter 3). Hosseinipooya, S. A., Hu, G., Li, J. (2022). A scenario-based decision-making framework for oil spill waste management strategies. Prepared for submission to a related journal. iii TABLE OF CONTENTS ABSTRACT..................................................................................................................................... i CO-AUTHORSHIP ....................................................................................................................... iii TABLE OF CONTENTS ............................................................................................................... iv LIST OF TABLES ......................................................................................................................... vi LIST OF FIGURES ...................................................................................................................... vii ACKNOWLEDGEMENT ........................................................................................................... viii CHAPTER 1 INTRODUCTION ................................................................................................. 1 1.1 Background ........................................................................................................................... 1 1.2 Objectives and Significance of The Study ............................................................................ 2 1.3 Organization of The Thesis ................................................................................................... 2 CHAPTER 2 LITERATURE REVIEW ...................................................................................... 3 2.1 Marine Oil Spill..................................................................................................................... 3 2.2 Oil Spill Response ................................................................................................................. 4 2.3 Oily Waste ............................................................................................................................. 5 2.4 Oil Spill Waste Management ................................................................................................ 7 2.4.1 Waste Quantity Estimation ............................................................................................. 7 2.4.2 Waste Management Strategies........................................................................................ 8 2.5 Decision-Making in Oil Spill Waste Management ............................................................... 8 2.6 Summary ............................................................................................................................... 9 CHAPTER 3 A SYSTEM DYNAMICS MODELING APPROACH FOR ESTIMATION OF OILY WASTE GENERATION FROM MARINE OIL SPILL RESPONSE: A CASE STUDY OF AN OIL SPILL IN CENTRAL COAST OF BRITISH COLUMBIA ................................... 11 Abstract ..................................................................................................................................... 11 3.1 Background ......................................................................................................................... 12 3.2 Methodology ....................................................................................................................... 15 3.2.1 Types of Oily Waste ..................................................................................................... 15 3.2.2 System Dynamics Model .............................................................................................. 16 3.2.3 Modeling Performance ................................................................................................. 23 3.2.4 Sensitivity Analysis ...................................................................................................... 23 3.2.5 Response Surface Methodology ................................................................................... 24 3.3 Case Study ........................................................................................................................... 24 3.3.1 Data Collection ............................................................................................................. 25 3.4 Results and Discussion ........................................................................................................ 27 iv 3.4.1 Estimation Results ........................................................................................................ 27 3.4.2 Sensitivity Analysis Results ......................................................................................... 31 3.4.3 Interaction Effects......................................................................................................... 34 3.5. Summary ............................................................................................................................ 36 Appendix A ............................................................................................................................... 40 CHAPTER 4 A SCENARIO-BASED DECISION-MAKING FRAMEWORK FOR OIL SPILL WASTE MANAGEMENT STRATEGIES ...................................................................... 41 Abstract ..................................................................................................................................... 41 4.1 Background ......................................................................................................................... 42 4.2 Methodology ....................................................................................................................... 44 4.2.1 Oil Spill Waste Management Strategies ....................................................................... 45 4.2.2 Impact Factors Selection .............................................................................................. 47 4.2.3 Hypothetical Data and Input Scenarios Generation ..................................................... 48 4.2.4 Optimization Model ...................................................................................................... 51 4.2.5 Data Collection for Fixed Modeling Parameters .......................................................... 55 4.2.6 Scenario-Based Decision-Making Framework ............................................................. 57 4.3 Case Study ........................................................................................................................... 58 4.3.1 Bella Bella Data ............................................................................................................ 58 4.3.2 Sensitivity Analysis ...................................................................................................... 59 4.4 Results and Discussion ........................................................................................................ 60 4.4.1 Suitable Oil Spill Waste Management Strategies in Different Scenarios .................... 60 4.4.2 Optimum Strategies for Bella Bella Oil Spill Waste .................................................... 63 4.4.3 Bella Bella Waste Quantity Sensitivity Analysis ......................................................... 65 4.5 Summary ............................................................................................................................. 67 Appendix B ............................................................................................................................... 68 CHAPTER 5 CONCLUSION AND RECOMMENDATIONS ................................................ 69 5.1 Conclusion........................................................................................................................... 69 5.2 Recommendations ............................................................................................................... 71 REFERENCES ............................................................................................................................. 72 v LIST OF TABLES Table 3.1. Types of oily waste considered in the system dynamics model.................................. 15 Table 3.2. Bella Bella oil spill data and assumptions for the system dynamics model parameters ....................................................................................................................................................... 25 Table 3.3. Comparing the results of the model and observation for the Bella Bella oil spill ..... 31 Table 3.4. Sensitivity analysis of the effect of response arrival time on recovered oil, oily water, and solid waste .............................................................................................................................. 33 Table 3.5. Sensitivity analysis of the effect of sorbent booms on recovered oil from sorbents and solid waste ..................................................................................................................................... 33 Table 3.6. Analysis of variance for estimation of total recovered oil (l) based on sea temperature (°C) and response arrival time (h)................................................................................................. 35 Table 3.7. Analysis of variance for estimation of total solid waste based on sorbent booms usage rate (m/12h) and sorbent boom weight (kg/m) .............................................................................. 36 Table 4.1. Impact factors in OSRW management ....................................................................... 48 Table 4.2. Hypothetical data and input scenarios generation for liquid oily waste management. 49 Table 4.3. Hypothetical data and input scenarios generation for solid oily waste management.. 50 Table 4.4. Variables and parameters for the liquid oily waste optimization model ..................... 53 Table 4.5. Variables and parameters for the solid oily waste optimization model ...................... 55 Table 4.6. Data collection for fixed modeling parameters in the optimization models ............... 56 Table 4.7. Bella Bella OSRW management data ......................................................................... 58 Table 4.8. The description of connectors in Fig. 3 ....................................................................... 62 Table 4.9. Optimum solutions for Bella Bella OSRW management strategies ........................... 64 Table 4.10. Decisions from the decision-making framework for the Bella Bella oil spill scenario …… 65 vi LIST OF FIGURES Fig. 3.1. Overview of the system dynamics model for estimating (a) recovered oil, (b) oily water, (c) solid waste collected from oil spill response ........................................................................... 17 Fig. 3.2. Modeling of (a) oil evaporation, (b) oil dispersion, (c) total recovered oil volume, and (d) recoverable oil as the function of time. ................................................................................... 29 Fig. 3.3. A contour plot for the estimation of total recovered oil (l) based on the interaction of sea temperature (°C) and response arrival time (h) in the Bella Bella oil spill................................... 34 Fig. 3.4. A contour plot for the estimation of total solid waste (kg) based on the interaction of sorbent booms usage rate (m/12h) and sorbent boom weight (kg/m) in the Bella Bella oil spill . 36 Fig. 4.1. Overview of the methodology framework ..................................................................... 45 Fig. 4.2. OSRW management strategies for (a) liquid oily waste and (b) solid oily waste.......... 46 Fig. 4.3. The developed scenario-based decision-making framework for OSRW management strategies ....................................................................................................................................... 61 Fig. 4.4. Sensitivity analysis for Bella Bella OSRW management strategies for (a) liquid oily waste and (b) solid oily waste ....................................................................................................... 66 vii ACKNOWLEDGEMENT I would like gratefully acknowledge my supervisor, Dr. Jianbing Li, who supported me at every stage of my study to complete my study and achieve my academic goals. Also, special thanks to my other committee members, Dr. Christopher Opio and Dr. Liang Chen, for their kind guidance and support throughout my research. I would also like to thank Dr. Guangji Hu for his unreserved support and helpful guidance in my research. I am also grateful to the other Dr. Jianbing Li students who provided me with insightful feedback and suggestions throughout my research. This research was financially supported by the Multi-Partner Oil Spill Research Initiative (MPRI) of Fisheries and Oceans of Canada and the Natural Sciences and Engineering Research Council of Canada (NSERC). My appreciation goes to Dale Bull, a senior environmental emergency response officer in the BC Ministry of Environment and Climate Change Strategy; Jeff Brady, deputy superintendent of environmental response from Canadian Coast Guard; Scott Wright, the response readiness director in WCMRC; and David Ellwood, a regional commercial manager from Terrapure Environmental who provided me with the information and data I needed for my research. I would like to dedicate my work to my charming wife, Zhaleh, whose support is always with me and has made my life lovely and beautiful, plus my parents and kind sister, who always accompanied me through all the challenges and successes of my life. viii CHAPTER 1 INTRODUCTION 1.1 Background About 5 million tonnes of world petroleum is transported by sea yearly (Richardson, 2004; Xiong et al., 2015). An oil spill as an environmental disaster is defined as releasing a liquid petroleum hydrocarbon into the environment, particularly the marine environment (Li et al., 2016; Zhang et al., 2019). Concerning human activities or national accidents, oil spills might lead to dire consequences (Li et al., 2016). Environmental, socio-economic, and public health impacts have been considered the most common adverse impacts of the marine oil spill (Li et al., 2016; Zhang et al., 2019). In the past decades, some immense marine oil spill accidents have occurred. For instance, from 1907 to 2014, more than 7 million tonnes of oil from about 140 large spills were released into the environment (Li et al., 2016). In 2010, just in one case, one of the largest marine oil spills in world history occurred when around 4 million barrels of crude oil were released into the Gulf of Mexico (EPA, 2022). This three-month spill not only killed a considerable population of marine animals but also reached the shoreline after months and affected a total of 790 km there (Al-Majed et al., 2012). Responsible organizations take various response and clean-up tactics to minimize the impacts of marine oil spills. However, the response operations usually result in a significant amount of hazardous waste, including liquid and solid oily waste streams. The generated waste is generally up to ten times more than the original spilled oil (IPIECA-IOGP, 2016; POSOW, 2016). Thus, oil spill response waste (OSRW) management, including effective strategies for waste treatment, disposal, and transportation, is considered the most complex and expensive part of an oil spill incident. Besides, various types and quantities of waste can be generated based on different factors, such as weather conditions, spilled oil characteristics, and type of response technologies. Decision-making about the most suitable options for each type of waste plays an essential role in an OSRW management and contingency plan. Moreover, before 1 selecting each treatment/disposal option, a realistic estimation of oily waste quantity is required. Oily waste quantity estimation is also difficult and complex (IMO, 2010; IPIECAIOGP, 2016). 1.2 Objectives and Significance of The Study This study aims to achieve two main research objectives. The first research objective is to estimate the quantity of different types of oily waste generated from offshore oil spill response operations over time through a system dynamics approach. The second objective is to develop a scenario-based decision-making framework for oil spill waste management strategies. The developed OSRW quantity estimation model assists response organizations and waste management companies in efficiently planning for handling each type of waste (e.g., transportation, treatment, and disposal) through monitoring the waste generation rate and analyzing tools provided by the first research objective. The second research objective helps related organizations and companies apply the most monetary beneficial waste management strategies for each type of waste under different conditions. The second objective, moreover, suggests a worthwhile area of investment for future OSRW management. 1.3 Organization of The Thesis The thesis structure is as follows: In chapter 2, the literature review was provided and summarized. The methodology, results, and discussion for the first and second objectives were described in chapters 3 and 4, respectively. In chapter 5, the conclusion of the whole study and the recommendations for the future was explained. 2 CHAPTER 2 LITERATURE REVIEW 2.1 Marine Oil Spill Although oil spills might be present in different environments, the marine environment has been clarified as the most challenging area for oil spills. While the oil remains in motion in the marine environment, it is generally static in the terrestrial. Also, the oil weathering process (e.g., evaporation, dispersion and emulsification) occurs rapidly in water rather than terrestrial (Ivshina et al., 2015). Ivshina et al. (2015) compared oil spill behaviour between the marine and terrestrial environments. They reviewed the oil spill problems and illustrated that the risk of future accidents of oil spills becomes higher in the marine environment. While most oil spill incidents are due to minor oil spills, most previous studies have focused on large oil spills, which bring about environmental and economic consequences. (Azevedo et al., 2014; Moroni et al., 2019). In contrast with large oil spills, studying small-scale ones is significantly more challenging as data collection, monitoring, and analysis are difficult. (Moroni et al., 2019). Environmental impact is one of the significant impacts of a marine oil spill, which must be minimized through response plans and decision-making frameworks (Li et al., 2016). The most impact of marine oil spill incidents regarding the environmental criteria is usually on oceanic quality and marine wildlife (Zhang et al., 2019). Hassler (2011) identified the importance of raising the influence of oil spill response actors on environmental safety and studied the effect of intelligent governance mechanisms on saving the environment after an oil spill. Cordes et al. (2016) reviewed marine oil spill environmental impacts during three phases of routine oil activities, including exploration, production, decommissioning, and accidental oil discharges. Besides, they reviewed existing environmental impact assessment approaches and related management methods to manage oil spills. Li et al. (2016) reviewed offshore oil spills, their environmental impact and some current response policies and technologies to deal 3 with oil spill situations. Their study has emphasized the significant importance of decision support systems and effective strategies for environmental impact reduction. Large oil spills can cause challenging and costly disasters. For instance, accidental leaks from shipping activities often lead to enormous economic costs (Shi et al., 2019). So, in addition to oil spill environmental impacts, the socio-economic impact of oil spills, including clean-up costs, environmental damage costs, research costs, and psychological impacts for residents, is essential. All the mentioned costs can be studied with monetary values (Li et al., 2016). Li et al. (2016) studied the socio-economic impact of the offshore oil spill in challenging situations. Besides, Zhang et al. (2019), along with indicating the existing challenges of socioeconomic cost estimation, reviewed the possible socio-economic impacts. 2.2 Oil Spill Response Marine oil spill response technologies aim to reduce the amount of spilled oil after incidents to decrease the possible damages (Chen et al., 2019). To date, four primary categories, including mechanical (skimmers, booms, oil-water separators, and adsorbents), chemical (dispersants, emulsion breakers), in-situ burning, and bioremediation (bio-augmentation and bio-stimulation) have been considered regarding existing response technologies (Dave and Ghaly, 2011; Ivshina et al., 2015; Li et al., 2016; Chen et al., 2019; Hu et al., 2020; Mohammadiun et al., 2021). The clean-up technologies applied by oil spill response teams considerably influence the type and quantity of oily waste generated. So, making decisions regarding the possible response methods is an important part of each oil spill waste management and contingency plan. Besides, in the decision-making process of oil spill cleanup strategies, the infrastructure capacity of related waste management techniques and principles must be considered (IPIECA-IOGP, 2016). 4 Regarding response scenarios and strategies, oil type is one of the crucial factors for consideration (Etkin, 2004; Krohling and Rigo, 2009; Zafirakou et al., 2018). Through an integrated model, Etkin (2004) estimated oil spill response costs for four main oil types: light fuels, heavy oils, crude oils, and volatile distillates. Etkin (2004) also considered the response technologies as an impact factor in oil spill costs and provided a methodology to specify the oil removal effectiveness of each response technology. Dave and Ghaly. (2011) evaluated the advantages and disadvantages of different oil spill response options by performing a comprehensive analysis utilizing ten criteria to assign a specific score to each response method. Their results showed that mechanical and dispersants followed by bioremediation approaches had the most efficiency regarding marine oil spill response. Ivshina et al. (2015) summarized the new response knowledge and technologies to develop decision-making frameworks for oil spill response alternatives. Chen et al. (2019) reviewed the traditional and leading marine oil spill response technologies and indicated that different techniques could be utilized based on environmental conditions, the availability of resources, and cost estimation. 2.3 Oily Waste Marine oil spill response operations can rapidly generate and accumulate a large quantity of oily waste. In most oil spill incidents, relying on natural recovery is impossible, and some clean-up strategies are required. Emulsified oil, debris, and oily sand increase the oily waste quantity to several times more than the original spilled oil (IPIECA-IOGP, 2016). Some types of oil (e.g., crude oil or bunker fuel) generally cause more oily waste (IMO, 2010). Although the relationship between the volume of the oil spill and the generated oily waste is not deterministic, in some cases, clean-up activities result in up to 30 or 40 times more waste after the shoreline impact of oil spills (Massoura and Sommerville, 2009; IPIECA-IOGP, 2016). For example, in 1991, the oil tanker Erika, after breaking and sinking off, spilled around 20,000 tonnes of heavy oil on the coast of Brittany, which caused approximately 250,000 tonnes of 5 oily waste (Richardson, 2004). In another case, a large oil spill from the tanker Prestige sinking, despite recovering almost all the volume of spilled oil, generated four times more waste (Carro et al., 2008). Types of oily waste generated from oil spill clean-up operations and supporting activities can be classified into two main categories: solid waste and liquid waste (Massoura and Sommerville, 2009; IMO, 2010; IPIECA-IOGP, 2016). Specific types of oily waste are generated based on factors such as employed clean-up technologies and oil type. For instance, by deploying booms and skimmers from response vessels at sea, oily liquid waste, including oily water and recovered oil, is generated after oil recovery. Also, response operations generate oily solid waste, including oily vegetation, oily personal protective equipment (PPE), oily sorbent materials, animal carcasses, and oily flotsam and jetsam are generated (IPIECA-IOGP, 2016; Saleem et al., 2022). Illegal discharge of oily waste collected from oil spill response operations to the marine environment is a marine pollution source. The oily waste discharge brings dire environmental consequences such as seabirds and fishery resource mortality. Thus, available guidelines prohibit releasing recovered oil into the sea (Lin et al., 2007; O'hara et al., 2013). Besides, OSRW is generally categorized as a hazardous material. So, there are many limitations and regulations for possible disposal options to reduce the environmental impacts of oily waste. As hazardous waste, decision-making about transportation, storage, and treatment alternatives of oily waste is complex and requires governmental monitoring and permission in most countries (IPIECA-IOGP, 2016). In addition to the environmental aspect, in terms of economic and public health, oily waste risk assessment is an essential part of oil spill waste management. Due to the capacities and limited infrastructures, the costs of each waste treatment/ disposal option and each transportation strategy might differ (Smith et al., 2011; IPIECA-IOGP, 2016). 6 2.4 Oil Spill Waste Management Management of waste generated from oil spill response operations is an oil spill incident's most complex and costly part. Any oil spill contingency plan should contain helpful information and guidelines for oily waste management vital components. OSRW waste management includes optimum decisions and strategies for waste minimization, final disposal and treatment options, short-term and long-term storage location, and waste transportation. Besides, to achieve the best alternatives for each component of oily waste management, it is essential to consider the infrastructure capacities, budget limitations, and environmental regulations (Richardson, 2004; IPIECA-IOGP, 2016). Despite various environmental and economic aspects and limitations of remediation methods, such as potential groundwater and air pollution and long-term management requirements, it is challenging to find the most environmental-friendly and cost-effective waste management strategies (Massoura and Sommerville, 2009; Saleem et al., 2022). 2.4.1 Waste Quantity Estimation An estimation of OSRW quantity is essential to identify the necessary waste management resources. The estimation is needed to plan for waste treatment/disposal strategies, waste transportation, and storage facilities. However, precise estimation is impossible due to the uncertainty of factors affecting the waste volume. The volume of spilled oil, oil spill fate, and employed clean-up techniques influence oily waste quantity generated from response actions. Generally, the portion of solid waste generated by physical clean-up techniques is much more than liquid waste (IPIECA-IOGP, 2016). The lack of oily waste quantity estimation is an issue in oil spill response and waste management contingency plans. So, researchers are trying to develop computer-based models and assessments for oily waste quantity estimation (Secretariat and Island, 2009; IMO, 2010; IPIECA-IOGP, 2016). 7 2.4.2 Waste Management Strategies Treatment strategies are subject to be selected in the decision-making process of oily waste management to reduce the waste volume and hazard, recycle the waste, and increase the waste value through energy recovery and conversion to a market product. In general, oily waste treatment options are reduction, reuse, and recycling activities (Massoura and Sommerville, 2009; IPIECA-IOGP, 2016). The three main categories of oily waste treatment techniques are thermal, biological, and physic-chemical. Thermal options include incineration and coincineration methods, pyrolysis/ thermal desorption, and oil reprocessing. Moreover, the bioremediation method used for natural oil breakdown is the most common biological treatment strategy. Also, approaches such as emulsion breaking, stabilization, beach washing, and sand washing are physio-chemical treatment options (Carro et al., 2008; Massoura and Sommerville, 2009; IPIECA-IOGP, 2016). Some methods are applied to treat oily waste before disposal options (IPIECA-IOGP, 2016). Waste disposal, as the final part of an oily waste management plan, consists of disposing of waste (e.g., in landfills) or using residual materials from pre-treatment and treatment actions. However, strict regulations and limitations exist as oily waste disposal may bring about environmental problems (Chaineau et al., 2002; Mandal et al., 2012; Vollaard, 2017). From discharging wastewater to the marine environment to disposing of oily waste at landfills, some disposal strategies are available to deal with oily waste. Besides, it might be possible to return sediments to the response site, recycle residual oily waste as a fuel source, or utilize treated waste in road construction (IPIECA-IOGP, 2016). 2.5 Decision-Making in Oil Spill Waste Management The decision-making process in the OSRW management aims to minimize the potential environmental or socio-economic risks after an oil spill (Liu and Wirtz, 2007). Concerning the complexity of making an appropriate selection of oily waste management strategies, oil spill 8 waste management can be defined as a difficult decision-making problem (Liu and Wirtz, 2007; Krohling and Companharo, 2011; Chen et al., 2019). The optimum decisions affect the contingency plan's success in OSRW management (Chen et al., 2019). In the case of the oil spill response and related waste management, combat strategies and alternatives exist in a decision-making analysis. The consideration of policies, regulations, and available contingency plans is required to develop an integrated model and decision support system for OSRW management options (Chen et al., 2019). Some OSRW decision-making frameworks and oily waste management systems have been introduced to date to provide effective oil spill response strategies and optimal management decisions (Li et al., 2019). However, the provided models mainly focus on oil spill response rather than waste management (e.g., French-McCay (2004); Ko and Cang (2010); Jing et al. (2012); Li et al., (2014); Zafirakou et al., (2018); Shi et al. (2019); Mohammadiun et al., (2021)). 2.6 Summary After a marine oil spill, responders utilize various technologies, including mechanical, chemical, in-situ burning, and bioremediation, to recover the spilled oil and clean up the environment (Hu et al., 2020; Mohammadiun et al., 2021). However, the response operations result in the generation of a significant amount of liquid and solid oily waste concerning the oil weathering process, reaching the shoreline, and mixture with debris (IPIECA-IOGP, 2016). The management of OSRW, such as transportation, treatment, and waste disposal, is the most challenging part after an oil spill (Massoura and Sommerville, 2009; Saleem et al., 2022). An estimation of the quantity of each type of waste is required to manage the OSRW efficiently (IPIECA-IOGP, 2016). Treatment technologies (e.g., thermal, biological, and physicchemical) and disposal options (e.g., landfill) are strategies in OSRW management (Carro et al., 2008; Massoura and Sommerville, 2009; IPIECA-IOGP, 2016). A decision-making 9 problem can optimize the environmental risks or socio-economic costs to find the optimum strategies to deal with each OSRW type (Chen et al., 2019). However, studies have mainly focused on oil spill response strategies rather than OSRW management and related strategies. 10 CHAPTER 3 A SYSTEM DYNAMICS MODELING APPROACH FOR ESTIMATION OF OILY WASTE GENERATION FROM MARINE OIL SPILL RESPONSE: A CASE STUDY OF AN OIL SPILL IN CENTRAL COAST OF BRITISH COLUMBIA1 Abstract The understanding of waste generation is of critical importance for effective oily waste management in marine oil spill response operations. A system dynamics model was developed in this study to estimate the quantity of oily waste generated from marine oil spill response operations. Various aspects were considered, including weather conditions, spilled oil volume and characteristics, response time, and response methods. The types of oily waste include recovered oil, oily water, oily sorbents, oily personal protection equipment, and oily debris. The model was validated using data collected from an actual oil spill incident in British Columbia, Canada. The comparison of model estimation and observed results showed an average prediction accuracy of 86%. Sensitivity analysis was conducted to examine the impacts of two modeling parameters, including response arrival time and sorbent booms amount. Results of a case study indicated that initiation of response operations five hours earlier could increase oil recovery by 26%. Furthermore, sensitivity analysis highlighted a possible 45% overuse of sorbents, resulting in the generation of unnecessary oily solid waste. Response surface methodology (RSM) analysis was applied to analyze the interaction effect of model parameters on model outputs. Results showed a significant interaction between sea temperature and response arrival time on recovered oil and between sorbent boom weight and sorbent booms usage rate on solid waste. The developed model can provide an effective tool for informed waste management decision-making related to marine oil spill response operations. Keywords: Marine oil spill response, oily waste, response surface method, system dynamics model, sensitivity analysis 1 This work has been published as Hosseinipooya, S. A., Hu, G., Lee, K., Li, J., Ng, K. T. W., & Vu, H. L. A system dynamics modeling approach for estimation of oily waste generation from marine oil spill response: A case study of an oil spill in Central Coast of British Columbia. Frontiers in Environmental Science, 801. 11 3.1 Background Marine oil spills can result from maritime accidents or human activities, such as transportation, drilling, storing, manufacturing, and even illegal oily waste discharge into the ocean (Gong et al., 2014). It was reported that more than 7 million tonnes of oil from about 140 large spills were released into the marine environment from 1907 to 2014 (Li et al., 2016). In 2010, the Deepwater Horizon spill released around 4 million barrels of oil to the Gulf of Mexico, representing the largest marine oil spill incident in history, which resulted in an environmental disaster and killed a large population of marine animals (EPA, 2022). As spills can cause significant environmental, economic, and health consequences, immediate response actions are usually applied on the sea (offshore) and on shorelines (Li et al., 2016; Chen et al., 2019; Zhang et al., 2019). The four most commonly actively used response methods are (i) mechanical containment and recovery through the use of skimmers, booms, oil-water separators, and/or adsorbents; (ii) use of chemical dispersants; (iii) in-situ burning, and (iv) bioremediation (Dave and Ghaly, 2011; Ivshina et al., 2015; Hu et al., 2020; Mohammadiun et al., 2021). Mechanical containment and recovery is the primary response option of choice globally due to its capability to recover spilled oil and its low environmental effects (Chen et al., 2019; Hu et al., 2020). In general, the type and volume of oily waste generated from oil spill response operations mainly depend on the type and volume of spilled oil, the oil characteristics (e.g., oil viscosity), the ocean and weather conditions, the response time, and the utilized response methods (Massoura and Sommerville, 2009; IPIECA-IOGP, 2016). The type of oil due to weathering processes like dispersion or evaporation and their effect on the chance of spilled oil reaching the shoreline may affect the quantity, quality, and types of generated waste (IPIECA-IOGP, 2016). The selection of response methods depends on different factors, such as oil type, and the response method used considerably affects the quantity and types of waste generated from 12 the response operation. For example, mechanical containment and recovery, as the most common oil spill response method, generates a large amount of oily waste, including liquid waste (e.g., emulsified oil and oily water) and solid waste (e.g., oily sorbents, oily debris, and oily personal protective equipment (PPE)) (Saleem et al., 2022). The generated waste can be ten times more than the original oil spill volume, which presents a significant waste management challenge (IPIECA-IOGP, 2016; POSOW, 2016). Oily waste management comprises minimization, short-term and long-term storage, transportation, treatment, and final disposal (IPIECA-IOGP, 2016; POSOW, 2016). For effective waste management, estimating waste quantity is of fundamental importance. This is a challenging task since many factors affect the rates and type of waste generated (IPIECAIOGP, 2016; POSOW, 2016). Numerous researchers have developed computational models and tools to support strategies for oily waste management. IMO (2010), as a guideline for oil spill waste management, identified various factors affecting the quantity of oily waste generated from response operations. Although the relationship between the initial spilled oil volume and the waste generation has been preliminarily identified based on historical accidental oil spills (Wadsworth, 2014; IPIECA-IOGP, 2016), there is still a lack of oily waste quantification models. Metcalf (2014) developed a location-based oil spill waste management plan to study the effect of response tactics and shoreline operations on the generated waste in different locations. Chen et al. (2021) developed a mathematical model to reduce oil spill waste management costs by optimizing the waste inventory allocation scheme under uncertainty. However, while their model provided waste management solutions, it could not accurately estimate the quantity of waste generation. Most previous studies focused on developing oil spill clean-up strategies, and very few studies were related to oily waste quantity estimation. System dynamics has been applied to analyze the nonlinear temporal behaviour of a complex system based on the causal relations among system components (Al-Khatib et al., 13 2016). Many studies have used system dynamics to investigate different systems, such as environmental and healthcare systems (Kollikkathara et al., 2010; Ciplak and Barton, 2012; Al-Khatib et al., 2016). For example, Al-Khatib et al. (2016) developed a system dynamics model regarding hospital hazardous waste management to determine the effect of various factors on hospital waste segregation. Vensim® and STELLA® are the commonly used software tools for system dynamics modeling (Kollikkathara et al., 2010). Unlike machine learning-based methods such as neural networks (Vu et al. 2021, 2022), system dynamics modeling does not require big data. This would make it a suitable approach for oil spill response waste (OSRW) estimation where there is a considerable lack of waste generation data from historical oil spills. To the best of our knowledge, system dynamics modeling has not been used for OSRW estimation. This study aims to develop a system dynamics model to estimate the quantity of different types of oily waste generated from marine oil spill response. Various aspects are considered in the model development, such as the response methods used to recover spilled oil, the equipment and number of vessels, weather condition, response time, initial oil spill volume, and ocean conditions. The data collected from an actual oil spill incident in British Columbia, Canada, was used to validate the modeling performance. Although the developed system dynamics model is validated through a case study of a diesel spill, the model and mathematical equations can be applied to other oil spills. The proposed system dynamics modeling approach would provide an effective tool for quickly quantifying oily waste generation from marine oil spill response under different conditions, and the outcomes would be useful for the development of effective oily waste management strategies. Appendix A shows the list of paper abbreviations. 14 3.2 Methodology By using STELLA® Professional (version 2.1.4) software, a system dynamics model was developed to estimate the quantity of offshore OSRW. The model did not consider oily wastes generated from shoreline clean-up and recovery. Because of the low waste generation of dispersant and in-situ burning and the strict environmental regulations limiting these methods, the model was mainly developed to account for the waste generation from offshore mechanical containment and recovery operations. 3.2.1 Types of Oily Waste Oily wastes generated from oil spill response operations and supporting activities can be classified into two categories, including oily solid waste and liquid waste (IPIECA-IOGP, 2016; POSOW, 2016). At-sea containment and recovery operations usually result in the generation of oily water, recovered oil, and solid waste. Solid waste may include oilcontaminated PPE, vegetation, sorbent materials, debris, and animal carcasses (IPIECA-IOGP, 2016; POSOW, 2016). During offshore response operations, the quantity of oily vegetation and animal carcasses is usually very low compared to other types of oily waste. Table 3.1 lists the oily waste types considered in the system dynamics model development. Table 3.1 Types of oily waste considered in the system dynamics model Type Recovered oil Definition The amount of original spilled oil collected from Category Liquid waste booming/skimming and sorbents Oily water Oily water from the decontamination of response vessels and Liquid waste equipment and the skimming process Oily sorbents Spent sorbent booms and pads Solid waste Oily PPE PPE contaminated with oil during response operations Solid waste Oily debris Wastes generated from flotsam and jetsam, as well as floated containers Solid waste 15 3.2.2 System Dynamics Model Fig. 3.1 presents the structure of OSRW estimation model. It includes different submodels to quantify recovered oil (Fig. 3.1a), oily water (Fig. 3.1b), and solid waste (oily PPE, oily sorbents, and oily debris) (Fig. 3.1c). A number of stock variables were used in the model, including “Recoverable oil (RO)”, “Recovered oil from skimming (RS)”, “Recovered oil from sorbents (RA)”, “Oily water (OW)”, “Oily PPE (OP)”, “Oily sorbents (OS)”, and “Oily debris (OD)”. 3.2.2.1 Sub-Model for Estimating Recovered Oil After an oil spill, responders try to recover as much spilled oil as possible. The mechanical containment and recovery operation usually includes booming operations coupled with skimmers and sorbents (ITOPF 2012; IPIECA-IOGP, 2016; POSOW, 2016). Although sorbent pads might be more effective on shorelines, in some offshore oil spills, especially when the type of original spilled oil is light, responders apply sorbent pads at sea to recover spilled oil. As a kind of boom with the absorption capability to recover oil, sorbent boom is the other common type of sorbent used in oil spill response operations (ITOPF, 2012). Different factors, including oil weathering, volume and type of spilled oil, and equipment specifications, can significantly affect the volume and quality of recovered oil (IPIECA-IOGP, 2016; Etkin and Nedwed, 2021). This study assumes that the total volume of recoverable oil after an oil spill is related to oil weathering processes (evaporation, dispersion), the rate of recovered oil from skimming, and sorbents. Moreover, to estimate the recovered oil as a portion of the original spilled oil, the water content in the recovered oil is considered. The water content in the recovered oil (water-in-oil emulsions) varies depending on the oil type and response time. The emulsified oil generally contains less than 50% of the water on the formation day (Fingas and Fieldhouse, 2003). In addition to water-in-oil emulsions, skimmers, depending on the efficiency, might collect water containing oil (oil-in-water emulsions) during the oil skimming. 16 (b) (c) 17 Fig. 3.1. Overview of the system dynamics model for estimating (a) recovered oil, (b) oily water, (c) solid waste collected from oil spill response (a) The total recovered oil (TR) [L3] is equal to the sum of the total recovered oil from skimming (TRS) [L3] and the total recovered oil from sorbents (TRA) [L3]. The total recovered oil from skimming (TRS) and from sorbents (TRA) equal the value of “Recovered oil from skimming (RS)” [L3T-1] and “Recovered oil from sorbents (RA)” [L3T-1] stocks, respectively, in the last unit time of response (Fig. 3.1a). Oil loss rate (OLR) [L3T-1] as the outflow for the “recoverable oil (RO)” [L3T-1] stock (Fig. 3.1a) defines the rate of decrease in the volume of recoverable oil remaining on the ocean due to the recovery operations and the oil weathering processes. The initial value of the RO stock equals the oil spill volume (OV) [L3]. Fig. 3.1a demonstrates that OLR is dependent on the evaporation rate (ER) [L3T-1] and dispersion rate (DR) [L3T-1] of oil on the ocean, the oil skimming rate (OSR) [L3T-1], and the oil sorption rate (OAR) [L3T-1]. In the system dynamics model, time t [T] refers to the time starting just after an oil spill incident until the end of the offshore response operations. The calculations of ER and DR are shown in Eqs. 3.1–3.4 (Fingas, 2011; Fingas 2014) = =( + =( = (3.1) × − × ) × ln (3.2) )× (3.3) µ . × (34.4 ∗ 6.3 × 10 ) . × ((0.032 × ( − 5))/ ) (3.4) where the evaporation ratio (EVR) (%) of the recoverable oil depends on the oil type, ocean temperature (T) (°C), and time after the spill (min); c and d are empirical parameters based on the oil type. Eq. 3.2 shows the evaporation equation for most oils. However, oils like diesel follow a square root equation2 (Fingas, 2011). The dispersion ratio (DIR) (%) is related to the 2 =( + × )×√ 19 oil viscosity (µ) (cp), wind speed (U) (m/s), wave height (H) (m), and wave period (W) (s) (Fingas, 2014). The oil skimming rate (OSR) [L3T-1] represents the inflow for the “Recovered oil from skimming (RS)” [L3T-1] stock. The initial value for the RS stock is assumed to be zero. OSR depends on the skimmer capacity (SC) [L3T-1], skimmer efficiency ratio (SE) (%) and the number of skimmers (NS) used in the response operations, as shown in Eq. 3.5 (Ye et al., 2019) = × × (1 + ) (3.5) where the SC represents the capacity of a skimmer to collect oil mixed with water on the ocean per unit of time (usually hour), and the SE represents the portion of oil in the collected oil/water mixture from skimming. The emulsification ratio (E) (%) represents the water content in the water-in-oil emulsions affecting the behaviour of spilled oil in the ocean and takes a different value for each type of oil (Fingas and Fieldhouse, 2003). The oil sorption rate (OAR) [L3T-1] represents the inflow for the “Recovered oil from sorbents (RA)” stock [L3T-1]. The initial value for RA is assumed to be zero. OAR depends on the usage rate of sorbent booms (SB) [LT-1] and sorbent pads (SP) [MT-1] and their sorption capacity symbolized by SBC [L3L-1] and SPC [L3M-1], respectively. It is calculated as Eq. 3.6 (Wei et al., 2003; Wang et al., 2018) = × + 1+ × (3.6) where SB and SP represent the rate of sorbents used in the oily recovery process after an oil spill. SBC and SPC, moreover, define the capacity of sorbents in the recovery of spilled oil on the ocean through the sorption process. 20 3.2.2.2 Sub-Model for Estimating Oily Water Oily water includes wastewater generated from the decontamination (washing) of vessels, PPE, and contaminated reusable booms, as well as the oil-containing water collected from the skimming process (Fig. 3.1b) (IPIECA-IOGP, 2016; POSOW, 2016). As shown in Fig. 3.1b, the oily water generation rate from decontamination (OWD) [L3T-1] is an inflow for “Oily water (OW)” stock [L3T-1]. It represents the rate of oily water generated from the decontamination process, assuming the initial value of the OW stock is zero. The calculation of OWD is shown in Eq. 3.7. = × + × + (3.7) × It can be found that OWD depends on the usage rate of reusable PPE (RP) [T-1] and the generated oily water from washing them (WRP) [L3], the number of response vessels (NV) and the oily water generation rate from washing them WV [L3T-1], the containment booms length (CB) [L] and the oily water generation rate from washing them (WB) [L3 L-1T-1]. Another inflow for the OW stock in Fig. 3.1b is the oily water generation rate from skimming (OWS) [L3T-1] which includes the rate of oily water collected from skimmers during the oil recovery operations. It is assumed that the oil/water mixture collected from skimming contains emulsified oil that can be recovered and oil-in-water emulsion, which might require further decanting. OWS is thus dependent on the skimmer efficiency ratio (SE) and oil skimming rate (OSR) [L3T-1], as shown in Eq. 3.8. = 1− (3.8) × 3.2.2.3 Sub-Model for Estimating Solid Oily Waste Three primary types of solid waste generated from oil spill response operations, including oily sorbents, oily PPE, and oily debris, were considered in the model. The total oily solid 21 waste (TSW) [M] is equal to the sum of total oily sorbents (TOS) [M], total oily PPE (TOP) [M], and total oily debris (TOD) [M]. TOS, TOP, and TOD equal the value of “Oily sorbents (OS)” [MT-1], “Oily PPE (OP)” [MT-1], and “Oily debris (OD)” [MT-1] stocks, respectively, in the last response unit time (Fig. 3.1c). Sorbent booms and pads are commonly used for sorbing spilled oil, and they would become oily sorbents. As shown in Fig. 3.1c, the oily sorbents generating rate (OSG) [MT-1], as the inflow for the “Oily sorbents (OS)” stock [MT-1], includes the sorbent booms waste generation rate (SBR) [MT-1] and sorbent pads waste generation rate (SPR) [MT-1]. The initial value of OS stock is assumed to be zero. As shown in Eq. 3.9, the SBR is calculated based on the usage rate of sorbent booms (SB) [LT-1], sorbent boom weight (SBW) [ML-1] as the original weight of each deployed sorbent boom, and the sorbent boom sorption capacity (SBC) [L3L-1] to sorb the emulsified oil in the ocean. The SPR is calculated considering the usage rate of sorbent pads (SP) [MT-1] and their sorption capacity (SPC) [L3M-1], as shown in Eq. 3.10 =( × = +( )+( × × × ) (3.9) × ) (3.10) is the density of oil [ML-3] at the sea temperature. where During response operations, personnel are often required to wear personal protective equipment (PPE), which creates a considerable quantity of solid waste after usage (IPIECAIOGP, 2016; POSOW, 2016). In Fig. 3.1c, the oily PPE waste generating rate (OPR) [MT-1] is the inflow for the “Oily PPE (OP)” stock [MT-1], assuming the initial value for this stock is zero. OPR is dependent on the number of responders (NR), the usage rate of non-reusable PPE (NP) per responder [T-1], and the weight of PPE (PW) [M], as calculated in Eq. 3.11. = × (3.11) × In Fig. 3.1c, the oily debris waste generating rate (ODR) [MT-1] is the inflow for the “Oily debris (OD)” stock [MT-1], assuming the initial value of OD is zero. ODR represents the 22 waste generation rate of oily debris from flotsam and jetsam (ODF) [MT-1] and containers (ODC) [MT-1] (IPIECA-IOGP, 2016), as calculated in Eq. 3.12 and Eq. 3.13 = × (3.12) = × (3.13) where VW is the weight of sunken vessel [M]; RF is the rate of flotsam and jetsam [T-1] (e.g., the estimated floating rate of sunken vessels that resulted in the flotsam and jetsam); NC is the number of cargos; RC is the floating rate of containers of each cargo [MT-1] that generates oily debris. 3.2.3 Modeling Performance The developed system dynamics model was validated using real-world offshore oil spill response data. The modeling accuracy h (%) is calculated below h= 1− | − | (3.14) where S is the model result, and O is the real-world observation. Eq. 3.14 validates the model performance in estimating the recovered oil, oily water, and oily solid waste as the outputs. 3.2.4 Sensitivity Analysis The sensitivity analysis is conducted to examine the impacts of modeling parameters on waste estimation results. It can also help decision-makers determine the desirable response alternatives. For instance, users can change the quantity of response materials like sorbents to see the variation of recovered oil and the quantity of generated oily solid waste. The analysis helps users determine the optimal usage of sorbents for maximization of oil recovery and minimization of waste generation. This study conducted the sensitivity analysis for all system dynamics model parameters to analyze their effect on the model outputs, including the recovered oil, oily water, and solid waste. Based on the expert knowledge and available 23 information regarding previous oil spills, a realistic range of values was selected for each parameter. Then, the sensitivity analysis for each parameter was run, and the results were recorded. As some parameters had more effects on the recovered oil, oily water, and solid waste, the results are presented for the most influential parameters of the model, including response arrival time and sorbent booms usage rate. 3.2.5 Response Surface Methodology Response surface methodology (RSM) is a statistical tool to analyze the relationships between two or more explanatory variables (model inputs) and one or more response variables. The main purpose of RSM might also be determining the optimum settings of effective inputs that result in the maximization/ minimization of response variables using some designed experiments (Khuri and Mukhopadhyay, 2010). RSM was conducted to analyze the interaction effect of different model inputs on the model outputs. The RSM results for the two most interactive parameters of the developed system dynamics model (sea temperature and response arrival time) on the total recovered oil as an important source of liquid waste are presented (section 3.4.3). The objective function of this RSM subjects to maximize the total recovered oil. The interaction of two other parameters with high interactivity (sorbent booms usage rate and sorbent boom weight) on total solid waste was also illustrated through RSM, with an objective function of minimizing the total solid waste. Minitab® (version 20.3) was used to perform RSM in this study. 3.3 Case Study An actual oil spill incident in Bella Bella, British Columbia, Canada, that occurred in October 2016 was considered as the case study for the model validation. During this incident, the fuel barge tug Nathan E. Stewart released approximately 110,000 l of diesel fuel into the marine environment after it ran to ground in Seaforth Channel, approximately 10 miles west of 24 Bella Bella. Responders took roughly one month to complete offshore response operations and remove the tug from the environment. During response operations, different types of waste were generated (TSB, 2018). 3.3.1 Data Collection The values of most input parameters of the system dynamics model for model validation were collected from the Bella Bella oil spill data, while a few other parameters used data based on previous studies. Table 3.2 presents the oil spill response data collected from different sources. Table 3.2 Bella Bella oil spill data and assumptions for the system dynamics model parameters Parameter (symbol) Oil spill volume (OV) Sea temperature (T) Empirical parameter (c) Value (unit) 110,000 l 11 °C 0.02 Empirical parameter (d) 0.013 Oil viscosity (µ) 2.56 cp Wind speed (U) Wave height (H) Wave period (W) Emulsification ratio (E) 5.14 m/s 0.5 m 1s <1 (%) Skimmers capacity (SC) Up to 32800 l/h total (If the recoverable oil remaining at sea is lower than the oil skimming capacity, considering skimmers efficiency, the capacity is up to the remaining recoverable oil) 2 (after response arrival) 95% Number of skimmers (NS) Skimmers efficiency ratio (SE) Remark Hourly average Based on the oil type used in the evaporation ratio equation Based on the oil type used in the evaporation ratio equation. Hourly average, calculated by the NOAA weathering model based on oil type. Hourly average. Hourly average. Hourly average. The water content in the waterin-oil emulsion is very low for diesel oil. The skimming process through the WCMRC vessel Eagle Bay started around 10 h after leaking oil into the marine environment (TSB, 2018). 25 Source TSB, 2018 TSB, 2018 Fingas, 2011 Fingas, 2011 NOAA, 2019 TSB, 2018 TSB, 2018 Fingas, 2014 Fingas and Fieldhouse, 2003 WCMRC website, 2021; Etkin and Nedwed, 2021 Hourly average. TSB, 2018 The efficiency ratio of the type of skimmers used in Bella Bella. WCMRC (Data was received in 2021) Parameter (symbol) Sorbent booms usage rate (SB) Value (unit) 271 m/12 h (Using sorbent booms started approximately 10 h after leaking oil into the sea). Sorbent pads usage rate (SP) 1.8 kg/12 h (Using sorbent pads started approximately 10 h after leaking oil into the sea). Sorbent boom sorption capacity (SBC) 9.93 l/m (If the recoverable oil remaining at sea is less than the sorption capacity, the capacity is estimated based on the remaining recoverable oil) 20 l/kg (If the recoverable oil remaining at sea is less than the sorption capacity of sorbents, the sorption capacity is estimated based on the remaining recoverable oil) Sorbent pad sorption capacity (SPC) Remark According to data provided by DFO, 15697 m sorbent booms were used over response operations. According to the incident information and experts' knowledge, we assumed that every 12 h, responders collect and use new sorbent booms (271 m). Based on the available information for total sorbent pads collected over the response operations. According to the incident information and experts’ knowledge, we assumed that every 12 h, responders collect and use new sorbent pads (1.8 kg). Based on the usual absorbency capacity of standard oil spill sorbent booms (SPC 5104/Bale) used in Bella Bella. Source DFO (Data was received in 2021) Based on the usual sorption capacity of standard polypropylene oil-only sorbents (used in Bella Bella). Melt Blown Technologies, 2021 TSB, 2018 WCMRC (Data was received in 2021) Reusable PPE usage rate (RP) 0 /24 h (after response arrival) Based on available information, there were just a few reusable PPE. So, it was assumed that the number of reusable PPE used by all responders per 24 h was zero. WCMRC (Data was received in 2021) Number of response vessels (NV) 45 TSB, 2018 Containment booms length (CB) 300 m (after response arrival) Oily water from washing reusable PPE (WRP) 10 l (if the recoverable oil exists) 45 response vessels were at the location during response operations. Overall, 300 m of containment booms were deployed over response operations. It was assumed that for washing each oily coverall, 10 l water was needed. 26 TSB, 2018 Estimated Parameter (symbol) Oily water rate from washing response vessels (WV) Oily water rate from washing containment boom (WB) Value (unit) 100 l/48h (if the recoverable oil exists) 5 l/m.48h (if the recoverable oil exists) Sorbent boom weight (SBW) 0.67 kg/m Oil density at the sea temperature ( ) Number of responders (NR) non-reusable PPE usage rate per responder (NP) 0.82 kg/l PPE weight (PW) 0.32 kg Sunken vessel weight (VW) 4,578,000 kg Rate of flotsam and jetsam (RF) 0 /h Number of cargos (NC) 0 Floating rate of containers of each cargo (RC) 0 kg/h Remark It was assumed that for washing each vessel, around 100 l water was needed per 48 h. It was assumed that for washing each meter of oily containment booms, 10 l water was needed per 48 h. Based on the standard oil spill sorbent booms used in Bella Bella (SPC 510- 4/Bale). Hourly average, calculated by the NOAA weathering model. 114 responders were at the field during response operations. According to information from WCMRC, each responder in BC oil spills usually wears a lightweight polyethylene-coated Tyvek fabric Deluxe Coverall. As an assumption, every other day, responders change their coveralls. The value is the weight of each lightweight polyethylene-coated Tyvek fabric Deluxe Coverall. The gross tonnage of the tug (Nathan E. Stewart) was 302, and the tank barge (DBL 55) was 4276. According to DFO, there was not any considerable flotsam or jetsam at Bella Bella. There were not any cargos at the Bella Bella oil spill accident. There were not any containers at the Bella Bella oil spill accident. 114 1 /48 h Source Estimated Estimated WCMRC (Data was received in 2021) NOAA, 2019 TSB, 2018 WCMRC (Data was received in 2021) WCMRC PPE’s supplier (Uline), 2021 TSB, 2018 DFO (Data was received in 2021) TSB, 2018 TSB, 2018 3.4 Results and Discussion The results of recovered oil, oily water and solid waste estimation, model validation, sensitivity analysis, and response surface methodology are presented in the following sections. 3.4.1 Estimation Results To validate the developed dynamic model, data in Table 3.2 was applied to estimate different types of products, including recovered oil, oily water, and solid waste (Table 3.1) 27 generated after the Bella Bella oil spill response operations. All estimated variables are timedependent in the dynamics model, and the start point is the time when the oil spill occurred. 3.4.1.1 Recovered Oil Fig. 3.2 shows the estimated variables relevant to recovered oil. Oil weathering processes (evaporation, dispersion, and emulsification) and the oil recovery using skimming and sorbents affect the volume of remaining recoverable oil and the total recovered oil. In the Bella Bella, oil spill incident resulted in the release of light oil (diesel) into the marine environment. Studies show that light oils like diesel usually need just a few hours to be evaporated and dispersed into the water column (Fingas, 2011; Fingas, 2014; NOAA, 2020). Moreover, weather conditions such as sea temperature, wind speed, and wave conditions influence the oil weathering process (Fingas, 2014). Fig. 3.2a illustrates the evaporation volume of oil over time. As shown, considering a dynamic value for recoverable oil volume, the evaporation rate increases to reach a maximum value and continues downward until oil exists in the seawater for recovery operations. Also, Fig. 3.2b illustrates the same trend for the dispersion volume of oil over time. Response operations in Bella Bella started approximately 10 h after the initial release of the diesel oil into the marine environment (TSB, 2018). Fig. 3.2c demonstrates that a small volume of spilled oil was recovered in total, as the majority of spilled oil was evaporated and dispersed before oil recovery operations started (i.e., 10 h after the spill). Overall, the total recovered oil from skimming and absorption operations in Bella Bella was 2,132 l (TSB, 2018). According to WCMRC, all the recovered oil in the Bella Bella was from using sorbents and the oil recovery through the skimming process was insufficient as very little recoverable oil had remained on the ocean when the skimmers started recovery operations. Fig. 3.2d shows that the volume of recoverable oil at sea, with a downward trend, reaches its steady state a few 28 hours after the oil spill, demonstrating that there was not a considerable volume of oil at sea after a few hours. (b) (a) (c) (d) Fig. 3.2. Modeling of (a) oil evaporation, (b) oil dispersion, (c) total recovered oil volume, and (d) recoverable oil as the function of time. 29 3.4.1.2 Oily Water and Solid Waste The dynamic model estimates the generation of approximately 6,000 l of oily water over the response operations (around one month) in the Bella Bella oil spill. The total volume of oily water depends on the decontamination process and parameters presented in Fig. 3.1b. The quantity of oily sorbents as a type of solid waste is dependent on the use of sorbent pads and sorbent booms. Although the oil recovery process through sorbents in Bella Bella took a few hours, the usage of sorbents continued until the last hours of operations to make sure that no remaining recoverable oil was left. By considering the weight of sorbents after oil sorption, the model estimates that approximately 13,300 kg of oily solid waste was generated due to the use of sorbents. The number of responders and the weight and usage rate of non-reusable PPE for each responder were considered to estimate the generated waste from oily PPE over time. So, the quantity of oily PPE continues to increase until the last hour of response operations. The model estimates around 547 kg of oily PPE after response operations in the Bella Bella. The quantity of oily debris as a stream of solid waste originates from oily flotsam and jetsam after an oil spill incident and the generated oily waste from floated cargos with their containers (IPIECA-IOGP, 2016). In the Bella Bella oil spill, there were no cargos or collected flotsam and jetsam; thus, the estimated value for oily debris is zero (RF, NC, and RC parameters take zero in Eq. 3.12 and Eq. 3.13). 3.4.1.3 Results Validation Table 3.3 compares the observed data in the real world and the model estimation. The estimated values illustrate the total collected waste over response time (almost 686 h). The data presented in the “Observation” column of Table 3.3 was provided by the Department of 30 Fisheries and Oceans Canada. The model validation shows a range of 85.2% to 87.7% accuracy for estimating each type of waste compared to the real data in the Bella Bella oil spill. Overall, the average accuracy of OSRW estimation is 86.3%. Based on the model results, the developed system dynamics model can be applied for the estimation of different types of waste generated from oil spill response operations. Table 3.3 Comparing the results of the model and observation for the Bella Bella oil spill Type of waste Model Observation Accuracy (%) Total recovered oil (TR) 1870 l 2132a l 87.7% 1.7% 1.94 % Oily water (OW) 6000 l Not-available - Total sorbent pads solid waste (SPS) 1252 kg 1456 kg 85.9% Total sorbent booms solid waste (SBS) 12040 kg Not-available - Oily PPE (OP) 547 kg Not-available - Oily debris (OD) 0 kg 0 kg Total solid waste (TSW) 13839 kg 823 bags c, 76.4%–94.1% approximately (Ave: 85.2%) Percentage of total recovered oil b 11199–13065 kg Accuracy average: 86.3%d a (TSB, 2018) (Total recovered oil (TR) / oil spill volume (OV)) c The weight of each bag is usually between 13.60 to 15.87 kg, according to WCMRC. d Considering the average accuracy for the total solid waste (85.2%), the accuracy average for the comparable outputs of the model is 86.3%. b 3.4.2 Sensitivity Analysis Results Table 3.4 shows the effect of the arrival time of response operations (the time of starting the oil recovery process by skimmers and sorbents) on the total recovered oil, generated oily water, and solid waste. As shown in Fig. 3.1 and explained in Table 3.2, the response arrival time affects the starting time of deploying skimmers and sorbents; the recoverable oil remaining on the ocean could be less due to oil weathering (Fig. 3.2d) if responders arrive late. The results demonstrate that if response operations in the Bella Bella oil spill had started earlier, 31 much more recovered oil could have been collected. For example, a five-hour decrease in the response arrival time could increase the percentage of recovered oil from around 2% to approximately 28% without a considerable increase in generated oily water and solid waste. The effect of response arrival time on the recovered oil is much more significant than on the generated oily water and solid waste, reflecting the economic and environmental importance of response time in oil spill cases. However, Table 3.4 illustrates that the decrease in response arrival time might generate more oily water as long as skimmers recover more recoverable oil remaining on the ocean, causing to collect of more water containing oil during the process of oil recovery. There is no significant change in the generation of oily water from decontamination as the total number of vessels and equipment departed to the oil spill location does not usually differ for the same volume of spilled oil and for a few hours change in response arrival time as long as the oil recovery process is required. However, the oil concentration in the collected oily water from decontamination might be more significant when the recoverable oil is more remarkable. The increase in response arrival time can also slightly reduce the generated solid waste by generating less oily sorbents from recovering less oil on the ocean. The weight of sorbents after oil sorption is greater than the original weight of sorbents. However, there is no significant difference regarding the generated solid waste among the first three rows in Table 3.4 as with the same quantity of sorbents used in Bella Bella and based on their capacity, the estimated volume of absorbed oil does not differ. Moreover, since in the Bella Bella, the use of sorbents and wearing a non-reusable coverall by responders lasted until the last hours of response operations, the generation of solid waste still exists even when there is low recoverable oil remaining on the ocean (Table 3.4). 32 Table 3.4 Sensitivity analysis of the effect of response arrival time on recovered oil, oily water, and solid waste a Response arrival time (h) 2 Total recovered oil (l) 59400 Oily water (l) 8960 Solid waste (kg) 14900 5 31400 7490 14900 8 6740 6190 14900 10a 1870 6000 13800 12 408 6000 12400 Time of starting response operations in the Bella Bella oil spill. The sensitivity analysis also considered the effect of sorbent booms on the total recovered oil and the total generated solid waste (Table 3.5). According to the model estimation results for the Bella Bella oil spill, the total recovered oil using 15697 m (271 m/12h) sorbent booms was estimated to be 1870 l. However, based on the sensitivity analysis result, it seems that even a 45% decrease in the use of sorbent booms could recover the same volume of oil. It means that the model estimates that in the Bella Bella response, there was around 45% overuse of sorbents, which may result in the extra generation of 4750 kg of solid waste. Although the model estimation might not guarantee the same recovery efficiency by reducing sorbent use, it demonstrates that it is vital to consider the effect of sorbent use on the total recovered oil and generated solid waste in making suitable decisions. Table 3.5 Sensitivity analysis of the effect of sorbent booms on recovered oil from sorbents and solid waste a Sorbent booms (m/12h) 68 Recovered oil from sorbents (l) 1100 Solid waste (kg) 5330 135 1780 8510 140 1820 8730 143 1850 8880 147 1870 9050 160 1870 9560 210 1870 11500 271a 1870 13800 Sorbent booms used in the Bella Bella oil spill. 33 3.4.3 Interaction Effects Minitab designs 13 unblocked runs of experiments for two continuous factors over a certain region of values. Fig. 3.3 illustrates the interaction effect between sea temperature as a weathering condition-related parameter and response arrival time on the total recovered oil estimated for the Bella Bella oil spill. A range of values between 2 °C and 15°C for sea temperature and between 5 h and 15 h for response arrival time were selected based on the effect of each parameter on total recovered oil. Fig. 3.3. A contour plot for the estimation of total recovered oil (l) based on the interaction of sea temperature (°C) and response arrival time (h) in the Bella Bella oil spill As shown in Fig. 3.3, it is estimated that the maximum recovered oil can be obtained from the lowest response arrival time and sea temperature. Moreover, Fig. 3.3 presents that the effect of response arrival time on the oil recovery is much more significant with a lower sea temperature than with a high sea temperature. Table 3.6 demonstrates a significant interaction between sea temperature and response arrival time on the recovered oil, considering the low related P-value. Therefore, in some seasons, responders might arrange more equipment and vessels as their immediate action can be more crucial. 34 Table 3.6 Analysis of variance for estimation of total recovered oil (l) based on sea temperature (°C) and response arrival time (h) Source Model Linear Sea temperature Response arrival time Square Sea temperature*Sea temperature Response arrival time*Response arrival time 2-Way Interaction DF a Adj SS b Adj MS c F-Value d P-Value e 5 2,001,651,971 400,330,394 95.16 2.8 × 10 2 1,615,511,390 807,755,695 192.00 7.7× 10 1 755,551,952 755,551,952 179.59 3.0× 10 1 8,599,59,438 859,959,438 204.41 1.9× 10 2 344,444,504 172,222,252 40.94 1.4× 10 1 222,411,153 222,411,153 52.87 1.7× 10 Sea temperature*Response arrival time 1 166,351,422 166,351,422 39.54 4.1× 10 1 41,696,078 41,696,078 9.91 0.016 1 41,696,078 41,696,078 9.91 0.016 Error 7 29,449,818 4,207,117 Total 12 2,031,101,789 a “DF” represents the degree of freedom, b “Adj SS” means the adjusted sum of squares, illustrating the amount of variation explained by each component in the model. c “Adj MS” as the adjusted mean of squares is computed by dividing the “Adj SS” by “DF”. d “F-Value” is the model statistic, e “P-Value” is a statistical measurement used to validate the significance of results based on the typical significance level (0.05). Fig. 3.4 shows the interaction effect between sorbent booms usage rate and sorbent boom weight on total solid waste estimation for the Bella Bella oil spill. A range of values between 1 kg/m and 3 kg/m for sorbent boom weight and between 150 m/12h and 400 m/12h for sorbent booms usage rate were considered in RSM. Fig. 3.4 illustrates that both the sorbent boom weight and sorbent booms usage rate positively correlate with total solid waste. Also, the effect of sorbent booms usage rate on total solid waste is more considerable when the sorbent boom weight takes a higher value. Table 3.7 demonstrates a significant interaction between sorbent booms usage rate and sorbent boom weight on total solid waste considering the low P-value. 35 Fig. 3.4. A contour plot for the estimation of total solid waste (kg) based on the interaction of sorbent booms usage rate (m/12h) and sorbent boom weight (kg/m) in the Bella Bella oil spill Table 3.7 Analysis of variance for estimation of total solid waste based on sorbent booms usage rate (m/12 h) and sorbent boom weight (kg/m) Source DF Adj SS Adj MS F-Value P-Value a Model 5 420,598,552 84,119,710 138,612.41 <1 × 10 Linear 2 402,713,502 201,356,751 331,795.55 <1 × 10 Sorbent booms usage rate 1 135,267,003 135,267,003 222,892.90 1 × 10 Sorbent boom weight 1 166,994,689 166,994,689 275,173.76 <1 × 10 Square 2 1387 694 1.14 0.372 Sorbent booms usage rate*Sorbent booms 1143 1143 1.88 0.212 1 usage rate Sorbent boom weight*Sorbent boom 110 110 0.18 0.683 1 weight 2-Way Interaction 18,854,943 18,854,943 31,069.17 4.9× 10 1 Sorbent booms usage rate*Sorbent boom 18,854,943 18,854,943 31,069.17 4.9× 10 1 weight Error 7 4,248 607 Total 12 420,598,552 84,119,710 13,8612.41 a “P-Value” shows a significant result if it is lower than the typical significance level (0.05). 3.5. Summary The management of OSRW is the most challenging, time-consuming and expensive part of marine oil spill response. The first step of OSRW management is estimating liquid and solid 36 waste quantity. The estimation of waste generation can assist decision-makers in selecting the most suitable waste management strategies, transportation methods, and the optimum location of storage, treatment, and disposal facilities. This study developed a system dynamics-based model to estimate different types of OSRW. The model considered the dynamic effect of a variety of aspects, including weather conditions, response equipment, and volume and characteristics of spilled oil. As the advantage of all dynamics models, the user can monitor and analyze the generation rate of each type of waste during the response operation. A case study of response operations following the Bella Bella oil spill incident was used to validate the model. The comparison results showed approximately 86% accuracy, on average, for the model outputs. Another importance of this study is that the developed system-dynamics model is highly suitable for the sensitivity analysis and the analysis of interaction effects of parameters on the generated OSRW. Sensitivity analysis was implemented for some model parameters to provide decision-makers with information to develop optimum OSRW management strategies. For example, the sensitivity analysis for response arrival time estimated that responders could have increased the volume of recovered oil by 26% by arriving at the spill location five hours earlier. This highlights the importance of quick response in terms of recovery of spilled oil. Also, the sensitivity analysis for the use of sorbent booms revealed the possibility of a 45% overuse of sorbents during the Bella Bella oil spill response operations that resulted in unnecessary solid oily waste generation. Besides that, the conducted response surface methodology illustrated the significant interaction effect of sea temperature and response arrival time on the total recovered oil; and sorbent booms usage rate and the sorbent boom weight on total solid waste estimated for the Bella Bella oil spill. 37 Appendix A Abbreviations RSM OSRW response surface methodology oil spill response waste SP SPC PPE personal protective equipment OWD RO recoverable oil RP RS recovered oil from skimming WRP RA recovered oil from sorbents NV OW oily water WV OP oily PPE CB OD oily debris WB TR total recovered oil OWS TRS TRA OLR OV ER total recovered oil from skimming total recovered oil from sorbents oil loss rate oil spill volume evaporation rate TSW TOS TOP TOD OSG DR dispersion rate SBR OSR OAR SBW SPR DIR oil skimming rate oil sorption rate time starting just after an oil spill incident until the end of the offshore response operations evaporation ratio ocean temperature an empirical parameter in the EVR calculation an empirical parameter in the EVR calculation dispersion ratio µ oil viscosity ODF U wind speed ODC H W SC SE NS E SB SBC wave height wave period skimmer capacity skimmer efficiency ratio number of skimmers emulsification ratio sorbent booms usage rate sorbent boom sorption capacity VW RF NC RC t EVR T c d oil density OPR NR oily PPE waste generating rate number of responders NP non-reusable PPE PW weight of PPE ODR oily debris waste generating rate oily debris generation rate from flotsam and jetsam oily debris generation rate from containers Sunken vessel weight rate of flotsam and jetsam number of cargos floating rate of containers per cargo accuracy of results model result real-world observation h S O 40 sorbent pads usage rate sorbent pad sorption capacity oily water generation rate from decontamination reusable PPE usage rate oily water from washing reusable PPE number of response vessels oily water generation rate from washing vessels containment booms length oily water generation rate from washing containment booms oily water generation rate from skimming total solid waste total oily sorbents total oily PPE total oily debris oily sorbents generating rate sorbent booms waste generation rate sorbent boom weight sorbent pads waste generation rate CHAPTER 4 A SCENARIO-BASED DECISION-MAKING FRAMEWORK FOR OIL SPILL WASTE MANAGEMENT STRATEGIES Abstract The management of oily waste generated from oil spill response is critical and challenging. This study developed a scenario-based decision-making framework to help select the most monetary appropriate strategies to deal with two types of oily waste (liquid and solid) under different conditions of impact factors, including the type of spilled oil, waste quantity, waste quality, and the capacity, location, and availability of treatment and disposal facilities. Based on the combination of impact factors, 1600 and 4608 input scenarios were generated for liquid and solid oily waste, respectively, to develop the framework. An optimization model with a net costs minimization objective was developed for each type of waste to evaluate each alternative to find the optimum strategy and address the quantity of waste assigned to each destination. To run the optimization models generated hypothetical data and actual data for fixed parameters were used. The analysis of results from comparing the optimum solution of all scenarios was applied to develop the scenario-based decision-making framework. Results showed that the appropriate strategy for liquid oily waste from spilled refined oil (e.g., diesel and bunker) is sending the waste to a processing facility for physical and chemical separation. For liquid oily waste from spilled crude oil, the most financially beneficial strategy is selecting the oil refinery as the destination if the waste quantity is above a defined limit explained as an equation in this study. For solid oily waste, the results showed that pyrolysis is the best option if available; otherwise, incineration is better than landfill disposal. A real case of oil spill response in British Columbia, Canada, was used to demonstrate the application of the developed model for each type of waste and validate the decision-making framework. The sensitivity analysis results highlighted the importance of investment in financially beneficial strategies like using pyrolysis facilities for solid waste treatment. Keywords: oil spill response waste, waste management strategies, optimization, scenariobased decision-making, sensitivity analysis 41 4.1 Background Environmental disasters caused by marine oil spills can negatively impact the environment, public health, and local economy (Li et al., 2016; Zhang et al., 2019). Offshore oil spills continuously happen worldwide despite significant progress in prevention measures (Zhang et al., 2019; Hu et al., 2020; Hosseinipooya et al., 2022). For example, more than 140 large oil spills occurred between 1907 and 2014 that released over 7 million tonnes of oil into the environment (Li et al., 2016). The largest oil spill in history is the Deepwater Horizon oil spill, where nearly 4 million barrels of oil were released into the Gulf of Mexico, resulting in enormous economic and environmental damages (EPA, 2022). After an oil spill, responders apply various methods, including mechanical containment and recovery, use of chemical dispersants, bioremediation, and in-situ burning to rapidly clean up the spilled oil and reduce the environmental impacts (Hu et al., 2020; Mohammadiun et al., 2021). However, some response actions usually result in the generation of a considerable amount of oily liquid and solid wastes (e.g., oil-contaminated seawater; oily PPE, sorbents, and debris) (Saleem et al., 2022; Hosseinipooya et al., 2022). Many factors, such as the type of spilled oil, weather conditions, response equipment efficiency, and the response arrival time, affect the quantity of generated oil spill response waste (OSRW) (Hosseinipooya et al., 2022). Studies have shown that the quantity of OSRW might be ten times more than the volume of the original spilled oil (IPIECA-IOGP, 2016; POSOW, 2016). Managing hazardous OSRW is one of the most challenging tasks after an oil spill response. Efficient OSRW management requires optimized decision-making regarding the waste's transportation, treatment, and disposal (IPIECA-IOGP, 2016; POSOW, 2016). For example, waste management companies must select whether to send oily liquid waste directly to wastewater treatment facilities or send it to a processing facility to separate oil from water for resource recovery. Moreover, many options are available for solid waste treatment and 42 disposal, such as landfilling, incineration, and pyrolysis. The selection of each option may have different cost and benefit implications. Depending on the oil type, waste quantity and quality, and the location and capacity of treatment/disposal facilities, the most monetary beneficial OSRW management strategy may differ. This highlights the need for a comprehensive decision-making framework considering suitable strategies under different conditions (IPIECA-IOGP, 2016; Chen et al., 2021; Saleem et al., 2022). Studies have mainly focused on oil spill response options, and only a few developed decision analysis systems are available for efficient waste management. Chen et al. (2021) developed an inventory theory-based model to optimize the location of OSRW management facilities and the quantity of waste allocated to each facility after an oil spill. The study has not optimized waste management strategies and excluded the possible benefits of different OSRW management strategies. Also, this study has not considered the effects of different conditions, such as waste quantity and quality, on waste management. Saleem et al. (2022) developed a lifecycle assessment-based framework to evaluate OSRW management strategies. Although their study can assist decision-makers in finding low-impact strategies for OSRW management, the focus is on environmental impacts rather than cost-benefit analysis. This study aims to develop a scenario-based decision-making framework to help identify the most appropriate OSRW management strategies under different conditions (e.g., waste quantity, waste quality, and location and capacity of facilities). The framework was developed using the analysis of results from the evaluation of input scenarios generated based on the combination of impact factors in OSRW management. An optimization model was developed for each waste type to find the optimum solution for each possible scenario. The optimization model is to minimize the total net costs. A case study of an actual oil spill incident in British Columbia, Canada, was applied to validate the performance of the decision-making framework and demonstrate the applicability of the developed optimization model. A sensitivity analysis 43 of possible waste management strategies for the case study was also conducted. To the best of our knowledge, there are few scenario-based decision-making frameworks based on applying an optimization model for OSRW management strategies. The development of such a framework is of high importance to reduce waste management costs and increase the chance of making revenue out of collected oily waste by taking the most suitable strategy under different conditions. In addition, the proposed framework could assist decision-makers in finding the optimum quantity of waste assigned to each strategy under different conditions after an oil spill. The outcomes would be helpful for the related waste management organizations to know about possible monetary beneficial opportunities in the area of OSRW management. 4.2 Methodology The management of waste collected after offshore oil spill response operations usually contains different steps, including setting up temporary storage, transporting the oily waste, and planning for treatment and disposal options as the final destinations of the oily waste (IPIECA- IOGP, 2016). The OSRW management strategies term used in this study frequently refers to treatment and disposal options for each type of OSRW as an essential part which affects all other steps in OSRW management. However, the suitable strategy might vary under different conditions as impact factors (e.g., waste quantity) can change the balance of cost and benefits. Fig. 4.1 presents an overview of the methodology of this study. In this study, each impact factor in OSRW management consists of some levels resulting in hypothetical data generation for each type of waste. Based on the combination of hypothetical data of all OSRW management impact factors, 1600 input scenarios for liquid waste and 4608 input scenarios for solid oily waste were generated. An optimization model was applied for each type of waste to minimize the net costs to give optimum decision variables (i.e., selected strategy and assigned waste quantity to each strategy) for each input scenario. In addition to hypothetical data, data 44 collection was needed for some fixed modeling parameters to run the optimization model for each input scenario. R studio (version 1.3.1093) was used to run the model and integrate input scenarios and the optimization model for each type of waste. R studio could report and store a results table in less than a few seconds, including all optimum decisions and the objective value for all input scenarios for each type of oily waste. Results from evaluating all input scenarios were then summarized into similar categories in terms of their effect on the selection of optimum strategies for each type of waste. For example, if the most appropriate strategy for a number of strategies was the same, those scenarios were categorized into a group. Based on the summary of results, some If Then rules were generated to develop a scenario-based decisionmaking framework for OSRW management strategies. The decision-making framework provides the most monetary beneficial strategies to deal with each OSRW under different scenarios (conditions). The detailed steps are presented in the following sections. Fig. 4.1. Overview of the methodology framework 4.2.1 Oil Spill Waste Management Strategies Fig. 4.2 presents OSRW management strategies addressed for liquid oily waste and solid oily waste. 45 (a) (b) Fig. 4.2. OSRW management strategies for (a) liquid oily waste and (b) solid oily waste Fig. 4.2a shows liquid oily waste management strategies. The type of spilled oil affects the final destination of the collected liquid oily waste as the oil refinery would not be an option for refined products such as diesel and bunker fuel oil (EIA, 2021). Liquid oily waste from spilled refined oil can be sent to a processing facility where physical and chemical separation methods are applied to separate the oil from water. The recovered oil can then be sold, and the separated wastewater must be treated before discharging into the environment. On the other 46 hand, another strategy is sending the liquid oily waste from spilled refined oil directly to wastewater treatment facilities. Although the expected costs of this strategy might be lower than the first one, there is no significant benefit (IPIECA-IOGP, 2016). For liquid oily waste from spilled crude oil, two strategies are i) sending the recovered crude oil to oil refineries for producing fuel oils (e.g., gasoline, distillate fuel oil, and kerosene-type jet fuel) (EIA, 2021) and ii) sending the oily waste directly to wastewater treatment facilities without oil recovery. Fig. 4.2b shows solid oily waste management strategies. In addition to landfills, associated with lower operation costs, solid oily waste can be sent to an incinerator or a pyrolysis facility for energy recovery. Although incineration and pyrolysis might be expensive, each has some expected benefits (e.g., selling electricity after incineration or selling bio-oil and bio-char resulting from pyrolysis) (IPIECA-IOGP, 2016). 4.2.2 Impact Factors Selection Table 4.1 lists the factors affecting the decision-making of OSRW management (IPIECA-IOGP, 2016). As presented in Fig. 4.2a, the strategies to deal with liquid oily waste may differ since for liquid oily waste from spilled refined oil (e.g., diesel and bunker fuel oil), the option of oil refinery, and for liquid oily waste from spilled crude oil, the option of the oil processing facility is impossible. Waste type is also considered an impact factor as OSRW management strategies for each type of oily waste differ. The other impact factor in OSRW management is waste quantity. The transportation and operational costs, plus the possible monetary benefits that can be achieved from taking each strategy, are directly related to the waste quantity of each type of oily waste. The other impact factor is waste quality, defined as the oil content in liquid oily waste and the organic or moisture content in solid oily waste. The oil content in liquid oily waste strongly affects the monetary benefits of selling recovered oil or refined products. The high organic or moisture content in solid oily waste may cause additional costs and difficulty in pyrolysis facilities (IPIECA- IOGP, 2016). The organic content in oily waste includes aromatics and polyaromatic hydrocarbons (PAHs) (Shie et al., 47 2000). Waste quality also may lead to the pre-treatment requirements, which can increase the costs for those strategies requiring quality improvement before final treatment or disposal. The location of treatment and/or disposal facilities is another factor affecting the decision-making about OSRW management strategies through its effect on transportation costs. The capacity and availability of treatment and/or disposal facilities are two impact factors that must be considered because of their effect on the total costs associated with each strategy. Table 4.1 Impact factors in OSRW management Factor Type of spilled oil Definition The type of spilled oil affecting liquid oily waste management strategies Waste type The type of OSRW, including liquid and solid wastes Waste quantity The quantity of each type of oily waste Waste quality The oil content in liquid oily waste and the ratio of organic or moisture content plus the requirement of pre-treatment in solid oily waste Location of treatment and/or disposal facilities The capacity of treatment and/or disposal facilities Availability of treatment and/or disposal facilities The location of treatment and/or disposal facilities affecting their distance from the oil spill location The capacity of treatment and/or disposal facilities to receive the oily waste The availability of required treatment and/or disposal facilities without the need to set up a new one 4.2.3 Hypothetical Data and Input Scenarios Generation Table 4.2 addresses levels of impact factors (Table 4.1) and illustrates hypothetical data and input scenarios generation in liquid oily waste management. As shown in Table 4.2, five levels of liquid oily waste quantity were addressed for each oil type based on the history of past oil spills reported in different studies. Also, four levels of the capacity of facilities, five levels of the oil content, two levels of the availability of treatment and disposal facilities, and four levels of facility location were addressed. Moreover, this study assumes that the total capacity of vehicles is not limited, as waste transportation (as an assumption, only road transportation was considered in this study) to the final destination is a must-do. However, each vehicle's 48 capacity was considered a fixed parameter, explained in the following sections. The value for the oil content presented in Table 4.2 is the oil ratio in the collected oily water (liquid waste), usually 10% to 55% (Saleem et al., 2022). The combination of different levels of all impact factors results in 16003 input scenarios for liquid oily waste management. Table 4.2 Hypothetical data and input scenarios generation for liquid oily waste management Factor Oil type Waste quantity 50 m3 500 m3 Crude oil Level (Hypothetical data) Oil content 10% < > 20% 5000 m3 < > 40% 10000 m3 > > 55% 50 m3 < < 10% 3 1000 m 500 m3 Refined oil Capacity of facilities < < 3 1000 m 30% 20% < > 30% 5000 m3 < > 40% 10000 m3 > > 55% Availability of facilities All facilities are available The oil refinery is not available All facilities are available The processing facility is not available Location of facilities = 800 = 800 = 800 = 1600 = 1600 = 800 = 1600 = 1600 = 800 = 800 = 800 = 1600 = 1600 = 800 = 1600 = 1600 : Liquid oily waste quantity (m3) : Capacity of the oil refinery facility (m3) : Capacity of the wastewater treatment and disposal facility (m3) : Capacity of processing facility (m3) : Distance between the oil spill location and the oil refinery (km) : Distance between the oil spill location and the wastewater treatment and disposal (km) : Distance between the oil spill location and the processing facility (km) Table 4.3 shows the addressed levels of impact factors and hypothetical data and input scenarios generation for solid oily waste management. The combination of levels of all factors affecting solid oily waste management (i.e., three for waste quantity, eight for capacity of 3 Crude oil input sceanrios (5 × 4 × 5 × 2 × 4) + (5 × 4 × 5 × 2 × 4) = 1600 49 facilities, two for pre-treatment requirements, two for moisture or organic content, three for availability of facilities, and sixteen for location of facilities) results in 4608 input scenarios for solid oily waste management. In Table 4.3, the pre-treatment method for solid oily waste is assumed the screening, which is a common method selected based on grain size (IPIECAIOGP, 2016). Also, the moisture or organic content presented in Table 4.3 is defined as high if the moisture content in solid oily waste is above 10 WT% or the organic content is above 70 WT%, increasing an additional cost for pyrolysis plants. The moisture or organic content in solid oily waste is defined as low if the moisture content is lower than 10 WT% and the organic content is 70 WT% or lower (IPIECA-IOGP, 2016; Zaman et al., 2017). Table 4.3 Hypothetical data and input scenarios generation for solid oily waste management Waste quantity 500 tonnes Capacity of facilities Pretreatment < < < Required < < > Level (Hypothetical data) Factor Moisture or Availability organic of facilities content High (Additional cost for the pyrolysis facility) All facilities are available < < > 50,000 tonnes < < > > > < Pyrolysis is not available Not Required Low (No additional cost for the 50 Location of facilities = 800 = 800 , , = 800 = 1600 , = 800 = 1600 , = 800 = 800 , = 800 = 1600 , = 800 = 1600 = 800 , = 1600 , = 800 = 1600 = 800 = 1600 , , = 800 = 1600 = 800 = 1600 , , = 800 = 1600 = 800 = 1600 , , = 800 = 1600 = 800 = 800 , , = 800 = 800 , = 800 = 800 , = 800 = 1600 , = 800 = 1600 , = 800 = 1600 Waste quantity Capacity of facilities Pretreatment > > < Factor Moisture or Availability organic of facilities content pyrolysis facility) = 800 = 800 Incineration and pyrolysis are not available > > < 100,000 tonnes Location of facilities > > > , , = 800 = 1600 = 1600 = 1600 , , = 1600 = 800 = 1600 = 1600 , , = 1600 = 800 = 1600 = 1600 , , = 1600 = 800 = 1600 = 1600 , = 1600 = 1600 , , , , = 1600 = 800 = 1600 = 1600 : Solid oily waste quantity (tonne) : Capacity of the landfill (tonne) : Capacity of the incineration facility (tonne) : Capacity of the pyrolysis facility (tonne) : Distance between the oil spill location and the landfill (km) : Distance between the oil spill location and the incineration facility (km) : Distance between the oil spill location and the pyrolysis facility (km) : Distance between the oil spill location and the pre-treatment facility (km) 4.2.4 Optimization Model An optimization model was developed for each type of OSRW (i.e., liquid and solid) to evaluate each input scenario generated based on the combination of different levels of impact factors described in Table 4.2 and Table 4.3. As an assumption, only road transportation was considered in this study. The model for liquid oily waste is shown as follows: ( × )(1 + [ / +( ) = ]) + +( × ) −( × ) (4.1) Subject to: (1 − ) + ≤ (1 − ) (4.2) (4.3) ∀ + +( ) (4.4) ≤ (4.5) ≤ (1 − ) 51 (4.6) ≤ ≥0 ∀ (4.7) ∈ {0, 1} ∀ (4.8) The liquid oily waste model objects to minimize net costs, considering transportation costs, operation costs, set-up costs, and total financial benefits. The transportation cost in the objective function includes the cost to transport oily liquid waste from the oil spill location to selected treatment or disposal facilities. The more volume of liquid waste, the more trucks are requested as they have limited capacity. Both total transportation and operation costs are dependent on the quantity of waste. The set-up cost is a fixed cost that is added when a facility is unavailable. The total benefit depends on the waste quality (the oil content in the liquid oily waste) and the waste quantity. For example, in a processing facility, from the treatment of each unit of separated oil in the liquid oily waste, an expected income can be achieved after selling the recovered oil. Eq. 4.2 demonstrates that the total quantity of waste assigned to facilities must equal the collected liquid oily waste. If the type of spilled oil is crude oil, the oil refinery and wastewater treatment facilities are available options. When the spilled oil is refined oil (e.g., diesel), the processing facility and wastewater treatment are selectable. Moreover, Eq. 4.3 and Eq. 4.4 show that the assigned quantity to each selected facility can not exceed the capacity of the facility and the total capacity of vehicles. Eq. 4.5 illustrates that the oil refinery can be selected if the type of spilled oil is crude. Eq. 4.6, moreover, shows that the processing facility is selectable only if the type of spilled oil is refined oil. Eq. 4.7 and Eq. 4.8 represent the type of decision variables in the model. There is a binary variable for selecting the suitable facility and a variable for assigning the optimum waste quantity to each facility. All parameters and variables in Eqs. 4.1 to 4.8 are listed in Table 4.4. 52 Table 4.4 Variables and parameters for the liquid oily waste optimization model Type Index Variable Parameter Definition (unit) The index that shows facilities (i.e., 1 indicates the oil refinery, 2 represents the wastewater treatment facility, and 3 is the processing facility) A decision variable for the amount of liquid waste assigned to facility j (m3) A binary variable for the facilities selection taking one if the facility j is selected The unit road transportation cost for the liquid oily waste (US$/km) The distance between the oil spill location and the facility j for the liquid oily waste (km) The vehicle capacity of each truck for transporting liquid oily waste (m3) The unit treatment operation cost of the facility j for liquid oily waste (US$/m3) Set-up cost of the facility j for the liquid oily waste (US$) A binary parameter taking one if the facility j for the liquid oily waste is not available The oil content in liquid oily waste The financial benefit from the selling of each unit of recovered oil treated from liquid oily waste in facility j (US$/m3) A binary parameter taking zero if the type of spilled oil is crude and one if the type of spilled oil is refined oil (e.g., bunker fuel oil) The quantity of liquid oily waste (m3) The capacity of the facility j for the liquid oily waste (m3) The total capacity of vehicles for liquid oily waste transportation (m3) The optimization model for solid oily waste is shown as follows: ( × )(1 + [ + / +( ]) + ( × )(1 + [ × +( × ) ) / ]) + (4.9) − Subject to: (4.10) = ≤ (4.11) ∀ (4.12) ≤ (4.13) = 53 ≤ (4.14) ≤ (4.15) + (4.16) ≤2 ≥0 (4.17) ∀ (4.18) ≥0 ∈ {0, 1} (4.19) ∀ (4.20) ∈ {0, 1} The objective function of the solid oily waste model is minimizing net costs, considering transportation, operation, set-up, and damage costs (if the solid waste contains a high organic or moisture content) and total financial benefits. Eq. 4.10 demonstrates that the total waste assigned to all possible facilities (i.e., landfill, incinerator, pyrolysis) equals the quantity of solid waste. Eq. 4.11 and Eq. 4.12 show that the quantity of solid waste assigned to facilities must be lower than facilities and transportation capacities. Eqs. 4.13 to 4.15 assign the solid waste quantity to the pre-treatment facility, respecting the facility's capacity and transportation. Eq. 4.16 demonstrates that if pyrolysis is selected or pre-treatment is required, the solid waste must be sent to a pre-treatment facility before sending to the final destination. Eqs. 4.17 to 4.20 illustrate the decision variables of the solid oily waste model, including a binary variable for selecting facilities and a variable for the assignment of the optimum quantity of waste to each selected facility). Table 4.5 describes all variables and parameters in Eqs. 4.9 to 4.20. Table 4.5 Variables and parameters for the solid oily waste optimization model Type Index Variable Parameter Definition (unit) The index that shows facilities (i.e., 1 for landfill, 2 for incineration, and 3 for pyrolysis facility) 54 Type Index Variable Parameter Definition (unit) A decision variable for the amount of solid waste assigned to facility j (tonne) A binary variable for the facilities selection taking one if the facility j for solid oily waste is selected A decision variable for the amount of solid waste assigned to the pre-treatment (tonne) A binary variable for the pre-treatment selection taking one if the pre-treatment for solid oily waste is selected The road transportation unit cost for the solid oily waste (US$/km) The distance between the oil spill location and the facility j for the solid oily waste transportation (km) The vehicle capacity of each truck for transporting solid oily waste (tonne) The distance between the oil spill location and the pretreatment for the solid oily waste (km) The treatment operation cost of the facility j for each unit of solid oily waste (US$/tonne) The pre-treatment operation cost for each unit of solid oily waste (US$/tonne) Set-up cost of the facility j for the solid oily waste (US$) A binary parameter taking one if the facility j for the solid oily waste is not available Damage cost to the pyrolysis facility (US$) A binary parameter taking one, if the solid waste contains a high amount of one or both organic content or moisture content The financial benefit resulted from the treatment of a unit of solid oily waste in facility j (US$/tonne) The quantity of solid oily waste (tonne) The capacity of the facility j for the solid oily waste (tonne) The total capacity of vehicles for solid oily waste (tonne) The capacity of the pre-treatment facility for the solid oily waste (tonne) A binary parameter taking one, if the solid waste requires pretreatment 4.2.5 Data Collection for Fixed Modeling Parameters In addition to hypothetical data addressed in Table 4.2 and Table 4.3, actual data was required for some parameters of optimization models (i.e., liquid and solid) to evaluate input scenarios for OSRW management decision-making. Table 4.6 lists the approximate values of all fixed parameters of the models. 55 Table 4.6 Data collection for fixed modeling parameters in the optimization models Parameter (symbol) Transportation unit cost ( ) Truck capacity for liquid oily waste ( Value 1.86 US$/km on average ) 23 m3 on average Truck capacity for solid oily waste ( ) Treatment operation cost in an oil refinery ( ) Treatment operation cost in a wastewater treatment facility ( ) Treatment operation cost in a processing facility ( ) Operation cost in a landfill ( ) 23 tonnes on average Around 300 US$/m3 0.4 US$/m3 on average 0.1 US$/m3 on average 125 US$/tonne on average Treatment operation cost in an incinerator 180 US$/tonne on average ( ) Treatment operation cost in a pyrolysis facility ( ) Solid waste pre-treatment (screening) operation cost ( ) Oil refinery set-up cost ( ) Processing facility set-up cost ( Incineration set-up cost ( Pyrolysis set-up cost ( ) ) ) Damage cost of high organic or moisture solid waste for pyrolysis (D ) Oil refinery financial benefit of selling gasoline and distillate fuel ( ) 210 US$/tonne on average (estimated) 43 US$/tonne on average 500,000,000 US$ on average (estimated) for an oil refinery with up to 50,000 barrels/day refinery 285,000 US$ on average (estimated) for a facility with a capacity of up to 25000 m3/day 300,000,000 US$ on average (estimated) for an incinerator with a capacity of up to 700 tonnes/day 450,000 US$ on average (estimated) for a pyrolysis plant with a capacity of around 35 tonnes/day 63,000 US$ on average (estimated) 1983a US$/m3 (Gasoline and distillate fuel are 1585.03 US$/m3 on average) 1585.03 US$/m3 on average Processing facility financial benefit of selling recovered oil (e.g., bunker fuel and diesel) ( ) Waste incineration financial benefit of 60 US$/tonne on average selling electricity ( ) 56 Source OTA, 2019; Method, 2022 Nathanson, 2020; Curry Supply Co, 2022 Miller Waste Systems, 2022 Hui et al., 2020 KPMG, 2017; SSI, 2021; CostWater, 2022 Shepherd, 2007; CleanaWater, 2022 City of Vancouver, 2022; RDFFG, 2022; Statista, 2022 Waste to Energy International, 2015; Energy Justice Network, 2019; Gaia, 2020 Wright et al., 2010; Chung et al., 2012 Arina et al., 2014; Wstyler, 2019 CERI, 2014 Shepherd, 2007 Gaia, 2020; Hometown Dumpster Rental (2021) Beston, 2022 Beston, 2022 EIA, 2021; EIA, 2022a; EIA, 2022b EIA, 2022a; EIA, 2022b Wikipedia, 2022; EIA, 2022c Parameter (symbol) Value Source Pyrolysis financial benefit of selling bio- 570b US$/tonne The Digest, 2017; on average (estimated) USU, 2019; USDA, 2021 oil and bio-char ( ) a The refining output, including 50% gasoline and 27% distillate fuel, and so on, is 6.3% larger than the input (EIA, 2021). b 60-70WT% and 15-25WT% of waste in pyrolysis result in bio-oil (760 US$/m3) and bio-char (382 US$/tonne), respectively (USDA, 2021). 4.2.6 Scenario-Based Decision-Making Framework To develop the scenario-based decision-making framework for OSRW management strategies, generated input scenarios based on the combination of levels for all impact factors (Table 4.2 and Table 4.3) were evaluated through an optimization model programmed in RStudio. Algorithm 4.1 describes the developed system that integrates the input scenarios, optimization models, and the final decision-making framework (Fig. 4.1). Algorithm 4.1 was applied separately for each type of waste. As shown in this algorithm, for each type of waste, using the LPSolve package, the optimization model was programmed in Rstudio to give the optimum decisions for each input scenario. All input scenarios for each type of oily waste were evaluated in RStudio in a few seconds. RStudio combined and exported the results in a single file. The scenario-based decision-making framework was developed by categorizing results into similar groups with the same optimum selection of strategies and through the If and Then rules. Algorithm 4.1: Scenario-based decision-making framework development Input: Import LPSolve package; Import hypothetical data; Define fixed parameters; Number of input scenarios N; Create an empty list for optimum solutions; Create an empty list for objective values; for 1 ≤ ≤ do Read hypothetical data Set coefficients of the objective function Set matrix corresponding to coefficients of constraints by rows Set inequality/equality signs of each constraint Set right-hand side coefficients Find the optimum solution and its objective value using the LP function 57 Algorithm 4.1: Scenario-based decision-making framework development Save the optimum solution in the related list Save the objective value in the related list end for Output: Combine the results of all alternatives in a matrix format Save and export an excel file of results for each type of waste Developing a scenario-based decision-making framework by categorizing the exported results 4.3 Case Study To show the performance of the developed optimization models and validate the scenariobased decision-making framework, an actual oil spill incident that occurred in Bella Bella, British Columbia, Canada (2016) was considered as the case study. In the Bella Bella oil spill, around 110,000 l of diesel fuel was released into the marine environment (TSB, 2018). 4.3.1 Bella Bella Data Table 4.7 shows the actual data for the Bella Bella OSRW management. Data in Table 4.2 and Table 4.3 were used to run the optimization models. In Table 4.7, concerning the lack of information, the nearest facilities to the Bella Bella located in British Columbia, Canada, were selected for oily waste handling. Table 4.7 Bella Bella OSRW management data Parameter (symbol) Spiled oil type (T) Liquid waste quantity ( ) Value (unit) 1/refined oil 7.87 m3 Solid waste quantity ( ) 13.8 tonnes Oil content (OP) 20.7%a Wastewater treatment location Bella Bella, BC Remark Diesel fuel is a refined oil Estimated value Source TSB, 2018 Hosseinipooya et al., 2022 Estimated value Hosseinipooya et al., 2022 Based on estimation, out of 7.87 Hosseinipooya m3 of liquid oily waste in Bella et al., 2022 Bella, 1.87 m3 was recovered oil, and 6 m3 was oily water. It was assumed that the oil content of recovered oil was 55%, and the oil content of oily water was 10% (Saleem et al., 2022). Heiltsuk wastewater treatment British plant Columbia Local, 2022 58 Parameter (symbol) Processing facility location Value (unit) Burnaby, BC Landfill location Prince George, BC 2500 m3 Wastewater treatment capacity ( ) Processing facility capacity ( ) Landfill capacity ( 0 2500 m3/day to 25,000 m3/day (Approximated based on studies) 2500 m3/day to 25,000 m3/day (Approximated based on studies) Based on studies, it was assumed that the landfill is available for receiving a massive amount of waste per day. Assumption The approximate distance between the oil spill location and the nearest wastewater treatment facility The distance between the oil spill location and a close processing facility in British Columbia The distance between the oil spill location and the landfill in Prince George It was assumed that the pre-treatment was on shore It was assumed that pre-treatment was required for solid waste No set-up cost added to the model 1 Set-up cost added to the model 1 Set-up cost added to the model 0 It was assumed that the solid waste does not contain high organic or moisture content 2500 m3 Tremendous ) Pre-treatment capacity ( Wastewater treatment distance from the oil spill ( ) Unlimited 0.8 km ) Processing facility distance from the oil spill ( Landfill distance from the oil spill ( Remark Source A fully permitted water treatment Sumas, 2022a services facility Prince George's waste facility Sumas, 2022b 1152 km ) 861 km ) Pre-treatment facility distance 0 km from the oil spill ( ) Pre-treatment requirement (P) 1 Availability of the processing facility ( ) Availability of the Incineration ( ) Availability of the Pyrolysis facility ( ) Moisture or organic content (O) a . ( . . × 0.55) + ( . Kim et al., 2019 Kim et al., 2019 Google Map Google Map Google Map × 0.1) 4.3.2 Sensitivity Analysis Sensitivity analysis was conducted for the case study to analyze the effects of input parameters on the selection of strategies and the optimum amount of waste allocated to each facility. While the sensitivity analysis was implemented for all impact factors (Table 4.1), the results for each model are presented only for the most influential ones (e.g., waste quantity) selected based on their effect on selecting OSRW management strategies. 59 4.4 Results and Discussion 4.4.1 Suitable Oil Spill Waste Management Strategies in Different Scenarios The scenario-based decision-making framework for OSRW management strategies is the main output of this study (Fig. 4.3). Fig. 4.3 shows the decision-making framework developed by analyzing the results using Algorithm 4.1. In Fig. 4.3, each connector represents a level of an impact factor (Table 4.2 and Table 4.3) presented by a diamond (blue shapes), which indicates the decision step. The combination of different levels of impact factors forms input scenarios in OSRW management. A number defines each connector in Fig. 4.3 to clarify the scenarios effective in the decision-making framework using mathematical formulas developed in this study. Table 4.8 describes the connectors in Fig. 4.3. As shown in Fig. 4.3, the financially beneficial decision (orange shapes) for each type of waste under different scenarios might vary. For example, the best financial decision to deal with liquid oily waste from spilled refined oil is to send the oily waste to a processing facility as much as the capacity allows. If the processing facility is unavailable, the optimum decision depends on the quantity of liquid oily waste and its oil content. To manage liquid oily waste, if the type of spilled oil is crude oil and the oil refinery is available, the suitable strategy can be to send liquid oily waste to wastewater treatment or an oil refinery, depending on the waste quality. For solid oily waste, if pyrolysis is available, the most financially beneficial strategy is sending the oily waste to a pyrolysis plant. Otherwise, incineration can be the most suitable option depending on waste quantity. Fig. 4.3 demonstrates that, in some cases, it is worthwhile to invest in setting up new facilities for future OSRW management (purple shapes). 60 61 Fig. 4.3. The developed scenario-based decision-making framework for OSRW management strategies Table 4.8 The description of connectors in Fig. 4.3 Connector #1 #2 #3 #4 #5 #6 #7 #8 Description Liquid oily waste as a type of OSRW Crude oil as the type of original spilled oil ( = 0) Refined oil as the type of original spilled oil ( = 1) The processing facility is not available, and to be selected, a set-up cost is required ( = 1) The processing facility is available, and no set-up cost is required ( = 0) The quantity of liquid oily waste is more than the total capacity of the processing facility ( > ) The quantity of liquid oily waste is equal to or lower than the total capacity of the processing facility ( ≤ ) Oil content range for selecting wastewater treatment as the destination of waste with refined oil ×( ( − ≤ #9 − ( × × )+ ) Required oil content to make investing in a processing facility worthwhile even though there is a set-up cost for the processing facility ×( ( − > #10 )+ )+ − ( × × )+ ) Waste quantity range for selecting wastewater treatment as the destination of liquid waste with refined oil ≤ (( #11 × )− ×( − )+ − ) Waste quantity required to make investing in a processing facility worthwhile even though there is a set-up cost for the processing facility > (( #12 #13 #14 × )− ×( − )+ − ) The oil refinery is not available, and to be selected, a set-up cost is required ( = 1) The oil refinery is available, and no set-up cost is required ( = 0) Oil content range by which the oil refinery is not the financially beneficial destination ×( − )+ − ≤ #15 Oil content required to select the oil refinery as the financially beneficial destination ×( − )+ − > #16 The quantity of liquid oily waste is more than the total capacity of the oil refinery ( > ) 62 Connector #17 #18 #19 #20 #21 #22 #23 #24 #25 #26 #27 #28 #29 Description The quantity of liquid oily waste is lower than or equal to the total capacity of the oil refinery ( ≤ ) Solid oily waste as a type of OSRW The solid oily waste needs screening in a pre-treatment facility based on the grain size ( = 1) The solid oily waste does not require the pre-treatment based on the waste quality ( = 0) The pyrolysis facility is available, and no set-up cost is required ( = 0) The pyrolysis facility is not available, and to be selected, a set-up cost is required ( = 1) The quantity of solid oily waste is lower than or equal to the total capacity of the pyrolysis ( ≤ ) The quantity of solid oily waste is more than the total capacity of the pyrolysis ( > ) The organic content and moisture content in the solid oily waste are lower than or equal to 70 WT% and 10 WT%, respectively ( = 0) The organic content is greater than 70 WT%, or moisture content is greater than 10 WT% in the solid oily waste ( = 1) The incineration is not available, and to be selected, a set-up cost is required ( = 1) The incineration is available, and no set-up cost is required ( = 0) The quantity of solid oily waste required to make investing in pyrolysis worthwhile > ( #30 − − ×( − )+ − ) The range of solid oily waste quantity by which investment in pyrolysis is not beneficial ≤ ( #31 #32 − − ×( − )+ − ) The quantity of solid oily waste is more than the total capacity of the incineration ( > ) The quantity of solid oily waste is lower than or equal to the total capacity of the incineration ( ≤ ) 4.4.2 Optimum Strategies for Bella Bella Oil Spill Waste Table 4.9 shows the optimum solutions for the case study for each type of waste. Concerning the type of original spilled oil in Bella Bella (diesel), sending all liquid waste to the processing facility is the best option. The result validates the developed scenario-based decision-making framework as, according to Fig. 4.9, the best strategy for liquid oily waste with refined oil is selecting the processing facility. In Table 4.9, the objective value for liquid waste is negative, illustrating that the total expected benefits are greater than the total costs for the optimum solution. So, selecting the processing facility as the destination of liquid waste 63 collected after the Bella Bella oil spill could be financially beneficial in general. Also, Table 4.9 shows that after pre-treatment, the best strategy for solid oily waste is disposing of the waste in a landfill concerning the lack of pyrolysis and incineration in BC, as the developed framework presented in Fig. 4.3. In Table 4.9, the objective value for the solid waste is positive showing that the total costs are more than the total expected benefits for the optimum solution. Table 4.9 Optimum solutions for Bella Bella OSRW management strategies Oil refinery Liquid waste 0 Wastewater treatment 0.00 m3 0.00 m3 0 Landfill Incineration Processing facility 1 Pyrolysis 7.87 m3 Objective -470.8 US$ Pre-treatment Solid waste Objective 1 13.8 tonnes 0 0.00 tonnes 0 0.00 tonnes 1 13.8 tonnes 5528.4 US$ Table 4.10 shows the suggested decisions (strategies) for the Bella Bella oil spill scenario based on the scenario-based decision-making framework (Fig. 4.3). Comparing the results in Table 4.10 and the Bella Bella oil spill optimum solutions (Table 4.9) validates the decision-making framework in Fig. 4.3. As shown in Table 4.10, for Bella Bella oil spill scenario, the best decision to deal with collected liquid oily waste is sending waste to a processing facility. Also, based on the quality of liquid oily waste with low oil content, wastewater treatment could be the best financial destination if there was no available processing facility. To manage solid oily waste collected in the Bella Bella oil spill, the suggested decision for the waste's final destination is a landfill due to the lack of other facilities. In this case, setting up a pyrolysis plant is not financially beneficial for the amount of solid oily waste. 64 Table 4.10 Decisions from the developed decision-making framework for the Bella Bella oil spill scenario Bella Bella oil spill scenario (liquid oily waste) Impact factor Type of Waste Waste Availability of spilled oil quantity quality facilities Refined oil 7.87 m3 < 8.71a m3 Low oil content (20.7%) Both processing and wastewater treatment facilities were available Capacity of facilities Enough capacity for the waste quantity Decision Processing facility selected (Wastewater treatment was selected if the processing facility was unavailable) Bella Bella oil spill scenario (solid oily waste) Impact factor Decision Capacity of facilities Enough Landfill selected 13.8 tonnes Pre-treatment capacity for (Setting up pyrolysis Only landfill was available <1085b tonnes required the waste is not worthwhile for quantity this amount of waste) a Calculated based on the formula for connector 10 in Table 4.8 using data in Table 4.6 and Table 4.7 b Calculated based on the formula for connector 30 in Table 4.8 using data in Table 4.6 and Table 4.7 Waste quantity Waste quality Availability of facilities 4.4.3 Bella Bella Waste Quantity Sensitivity Analysis Fig. 4.4 shows the sensitivity analysis results for OSRW management strategies of the Bella Bella oil spill scenario. As shown in Fig. 4.4a, increasing the liquid waste quantity demonstrates that more financial benefits result from recycling the waste while the optimum strategy is unchanged. So, in large oil spills, not only can the costs be compensated, but also, by utilizing optimized waste management, some benefits can be gained from the collected waste. This highlights the importance of applying the most suitable strategy to deal with collected waste after an oil spill. Assuming there is a chance of investment for setting up a pyrolysis facility 2414 km from the Bella Bella oil spill, Fig. 4.4b shows that for the solid waste quantity of more than 1097 tonnes, pyrolysis is the most financially beneficial option despite the set-up cost. Demonstrating the developed decision-making framework represented in Fig. 4.3, Fig. 4.4b illustrates that it is worthwhile to invest in pyrolysis for future large oil spills. 65 (a) (b) Fig. 4.4. Sensitivity analysis for Bella Bella OSRW management strategies for (a) liquid oily waste and (b) solid oily waste 66 4.5 Summary After oil spill response operations, a significant amount of liquid and solid wastes are generated. A scenario-based decision-making framework considering impact factors (i.e., waste quantity, waste quality, the capacity of transportation, the capacity and location of treatment and disposal facilities, and the feasibility of strategies) was developed to help responsible organizations beneficially manage the collected OSRW at a minimum cost under different conditions. The analysis of results from the evaluation of several input scenarios generated based on the combination of hypothetical levels of all addressed impact factors for each type of oily waste led to the development of the decision-making framework. An optimization model for managing each type of oily waste was developed and programmed in Rstudio to evaluate each input scenario in terms of finding the net costs, optimum strategy, and optimum quantity of waste assigned to each strategy. Actual data was also collected for each optimization model's fixed modeling parameters to run the model. The results demonstrated that the processing facility as the waste destination is the best financially beneficial strategy for liquid oily waste from spilled refined oil (e.g., bunker fuel oil and diesel). The oil refinery is the optimum destination for liquid oily waste from spilled crude oil if the waste quantity exceeds a limit formulated and described in this study. For solid oily waste, pyrolysis is the best option if available; incineration is the second most financially beneficial destination for solid waste management. Moreover, the results demonstrated the importance of establishing the processing facility for liquid and the pyrolysis plant for solid oily waste management. The optimum solution and the sensitivity analysis for the Bella Bella oil spill scenario as the case study validated the developed models and scenario-based decision-making framework. The results showed that using the processing facility is the best strategy for Bella Bella with diesel oil to manage liquid oily waste, as suggested by the developed decision-making framework. Also, the sensitivity analysis results illustrated that if the quantity of solid oily waste in Bella Bella was more than 1097 tonnes, it was worthwhile to establish a pyrolysis facility nearby. 67 Appendix B Abbreviations PPE personal protective equipment OSRW oil spill response waste PAHs polyaromatic hydrocarbons q oily waste quantity CTF the capacity of the treatment/ disposal facility d distance of the final destination of oily waste from the oil spill location dp distance of the pre-treatment facility from the oil spill location TC unit road transportation cost X optimum oily waste quantity assigned to the final destination CVT the capacity of the vehicle (truck) TOP treatment operation cost for the unit of waste quantity SC facility set-up cost S a binary parameter for set-up a facility Y a binary variable for the selection of facilities OP oil content in liquid oily waste TFB The financial benefit from the treatment of unit waste quantity T a binary parameter for the type of spilled oil Z optimum oily waste quantity assigned to the pre-treatment facility POC pre-treatment operation cost for the unit of waste quantity DC damage cost for the pyrolysis facility O a binary parameter for the moisture or organic content in solid oily waste CPF the capacity of the pre-treatment facility P a binary parameter for the pre-treatment requirement N number of input scenarios 68 CHAPTER 5 CONCLUSION AND RECOMMENDATIONS 5.1 Conclusion After a marine oil spill and concerning response operations, a significant amount of waste, including oily liquid and solid waste streams, is usually generated, considerably more than the original spilled oil. Managing collected OSRW, such as transportation, treatment and disposal of the oily waste, is the most challenging part after an oil spill. This highlights the importance of finding optimum strategies to deal with each type of waste. However, efficient decision-making in OSRW management requires a relatively precise estimation of oily waste quantity. This study developed a system dynamics-based model to estimate the quantity of each type of oily waste generated after oil spill response operations, considering the effect of various dynamic aspects, including weather conditions, the spilled oil characteristics and volume, response arrival time and equipment usage rate, and some others. The model was implemented using data from an actual case study in 2016 in Bella Bella, BC, Canada, to validate the performance. The comparison of the model estimation with observed actual data of the case study illustrated an average accuracy of 86%. Sensitivity analysis for the most effective parameters of the model also was applied and showed that a five-hour faster response in Bella Bella could increase the amount of recovered oil by 26%. Moreover, sensitivity analysis depicted that possibly 45% of the overuse of sorbents generated an unnecessary amount of solid waste in Bella Bella. Response surface methodology also was applied and highlighted the significant interaction effect between sea temperature and response arrival time on recovered oil and between sorbent boom weight and sorbent booms usage rate on solid waste. In addition to an OSRW estimation model, this study developed a scenario-based decision-making framework to provide decision-makers with the most financially beneficial strategies to deal with each type of oily waste under different conditions of impact factors, 69 including the type of spilled oil, waste quantity, waste quality, availability of facilities, capacity and location of treatment and disposal facilities. The evaluation of several input scenarios generated from the combination of hypothetical levels of all addressed impact factors was analyzed to develop the framework. For each type of oily waste, an optimization model was developed and programmed in Rstudio to give the minimum objective value (net costs) and optimum solutions, including the most suitable strategies and the optimum amount of waste, assigned to each strategy for each input scenario. The results showed that the oil processing facility is the best financially beneficial destination for liquid oily waste from spilled refined oil. The oil refinery is the best option for liquid oily waste from spilled crude oil unless the waste quantity is lower than the defined limit in this study. Results illustrated that to deal with solid oily waste, pyrolysis and incineration in orders are the best strategies. Furthermore, this study highlighted the importance of investment in the processing facility and pyrolysis for future OSRW management. The scenario-based decision-making framework was validated by optimum solutions (i.e., selecting strategies and the amount of waste assigned to each strategy) and sensitivity analysis of a case study (Bella Bella oil spill scenario). One of the main contributions of this study in science was developing an estimation model for generated oily waste from oil spill response operations. Estimating OSRW is essential and critical before making any decisions regarding the management of collected oily waste. Responders, after an oil spill, need to estimate their possible amount of collected waste as soon as possible for quick and efficient planning of its management. As the review of previous studies revealed, no models were developed to estimate OSRW prior to this study. Besides, as the advantage of system dynamics approaches, the developed model allows decision-makers to consistently monitor the generation of different types of oily waste over response time to improve their actions and planning. The system dynamics model of this study can be applied as an OSRW estimation model for all oil spill cases regardless of their oil type, 70 weather conditions, and other factors. The other contribution of this study is developing a decision-making framework which provides oil spill responders and waste management companies with the most financially beneficial strategies to deal with each collected OSRW under different conditions. For example, as the results in this study illustrated, a different quantity of waste or waste quality may significantly change the optimum destination of oily waste. The study can be considered a comprehensive and effective tool for OSRW management, from estimating waste quantity to optimum decision-making about available treatment and disposal options under different conditions. 5.2 Recommendations As data collection was a limitation and one of the most challenging parts of this research, creating a comprehensive database for oil spill waste management is very important, especially in Canada. The lack of data can negatively affect the research progress in this area. Moreover, as the results of the first research objective highlighted the importance of response arrival time on the possibility of oil recovery, future researchers can focus on developing an optimization model for minimizing the response arrival time through the optimum allocation of response facilities. Also, as the study showed that some environmental factors like sea temperature are effective on the generation of waste or the chance of oil recovery after an oil spill incident, the effect of climate change on the severity and number of oil spill incidents can be considered in a research study in the future. Besides that, researchers can study various ways to prevent or reduce the number of oil spills and their further effect on waste generation. The study on making the OSRW treatment and disposal facilities more environmental-friendly also should be considered in future research. 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