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Acid rock drainage (ARD) is a major environmental problem that poses significant environmental risks during and after mining activities. A new methodology for environmental risk assessment based on probability bounds and a geochemical speciation model (PHREEQC) is presented. The methodology provides conservative and non-conservative ways of estimating risk of heavy metals posed to selected endpoints probabilistically, while propagating data and parameter uncertainties throughout the risk assessment steps. The methodology is demonstrated at a minesite located in British Columbia, Canada. The result of the methodology for the case study minesite shows the fate-and-transport of heavy metals is well simulated in the mine environment. In addition, the results of risk characterization for the case study show that there is risk due to transport of heavy metals into the environment. [ABSTRACT FROM AUTHOR]

Process operation is the most hazardous activity next to the transportation and drilling operation on an offshore oil and gas (OOG) platform. Past experiences of onshore and offshore oil and gas activities have revealed that a small mis-happening in the process operation might escalate to a catastrophe. This is of especial concern in the OOG platform due to the limited space and compact geometry of the process area, less ventilation, and difficult escape routes. On an OOG platform, each extra control measure, which is implemented, not only occupies space on the platform and increases congestion but also adds extra load to the platform.Eventualities in the OOG platform process operation can be avoided through incorporating the appropriate control measures at the early design stage. In this paper, the authors describe a methodology for risk-based process safety decision making for OOG activities. The methodology is applied to various offshore process units, that is, the compressor, separators, flash drum and driers of an OOG platform. Based on the risk potential, appropriate safety measures are designed for each unit. This paper also illustrates that implementation of the designed safety measures reduces the high Fatal accident rate (FAR) values to an acceptable level. [Copyright &y& Elsevier]

This study employs fuzzy regression and fuzzy multivariate clustering techniques to analyze arsenic-polluted water samples originating from acid rock drainage in waste rock dumps. The research focuses on understanding the complex relationships between variables associated with arsenic contamination, such as water arsenic concentration, pH levels, and soil characteristics. To this end, fuzzy regression models were developed to estimate the relationships between water arsenic concentration and independent variables, thus, incorporating the inherent uncertainties into the analysis. Furthermore, multivariate fuzzy k-means clustering analysis facilitated the identification of fuzzy-based clusters within the dataset, providing insights into spatial patterns and potential sources of arsenic pollution. The pairwise comparisons indicated the strongest correlation of 0.62 between soil total arsenic and pH, while the weakest correlation of 0.13 was observed between soil-soluble arsenic and soil iron, providing valuable insights into their relationships and impact on water arsenic levels. The associated uncertainties in the relationships among the variables were determined based on the degree of belongingness of each data point to various fuzzy sets. Three distinct clusters emerged from the analysis: Cluster 1 comprised Points 5, 6, and 7; Cluster 2 included Points 1, 2, 3, 4, 8, and 9; and Cluster 3 consisted of Points 10, 11, 12, and 13. The findings enhance our understanding of the factors influencing arsenic contamination to provide an effective mitigation strategy in acid rock drainage scenarios. This research also demonstrates the applicability and effectiveness of fuzzy regression and fuzzy multivariate clustering in the analysis of arsenic-polluted water samples. [Display omitted] • Arsenic-contaminated water through acid drainage from a gold mine was analyzed. • Fuzzy regression and clustering analysis, suited for small samples, were applied. • Fuzzy concepts addressed the vagueness of relationship between chemical components. • Understanding the relationships helps in adopting an efficient mitigation strategy. [ABSTRACT FROM AUTHOR]

Marine oil spill response is a time sensitive and complex task, in which the management of oily wastewater generated from response operations could be a bottleneck limiting the response capacity and efficiency. This study developed a multi-agent decision support system to effectively coordinate mechanical containment and recovery (MCR) of spilt oil and oily wastewater management (OWM) operations. The system aims to minimize the overall response time, cost, and the volume of weathered oil by applying evolutionary optimization, oil weathering process, and response operational agents. The multi-agent system was used to manage an incident through a hypothetical case study in Canada. The system enables the scheduling of available response assets considering all complex operational details of MCR and OWM, such as available oily wastewater storage and transportation capacities and the competitive skimming process of various response vessels. Implementing the evolutionary multi-objective optimization led to a considerable reduction in response time (about 13.6% or 67 hr), cost (about 1.4% or CA$24,800), and volume of weathered oil (about 9.2% or 19 m3) compared to the shortest distance approach. The developed multi-agent system can facilitate decision-making in complex marine oil spill response tasks for maximizing response efficiency. [Display omitted] • A decision support system is developed for effective marine oil spill management. • Oil spill response and oily waste management operations were optimized together. • Overall response time and cost, as well as volume of weathered oil were minimized. • The multi-agent system was tested on a comprehensive case study in Western Canada. • Multi-objective optimization approaches outperformed shortest distance model. [ABSTRACT FROM AUTHOR]

Abstract: Chlorination for drinking water forms various disinfection byproducts (DBPs). Some DBPs are probably linked to human cancer (e.g., bladder, colorectal cancers) and other chronic and sub-chronic effects. This emphasizes the need to understand and characterize DBPs in drinking water and possible risks to human health. In this study, occurrences of DBPs throughout Canada were investigated. Trihalomethanes (THMs) were observed to be highest in Manitoba followed by Nova Scotia and Saskatchewan, while haloacetic acids were highest in Nova Scotia followed by Newfoundland and Labrador. Based on the characterization of DBPs, risk of cancer from exposure to THMs was predicted using ingestion, inhalation and dermal contact pathways of exposure. In Canada, approximately 700 cancer cases may be caused by exposure to THMs in drinking water. Medical expenses associated with these cancer incidents are estimated at some $140 million/year. Expense may be highest in Ontario (∼$47 million/year) followed by Quebec (∼$25 million/year) due to a greater population base. This paper suggests improvements in water treatment, source protection and disinfection processes, and caution in the use of alternative disinfectants to reduce DBPs. Finally, elements are provided to mitigate risks and reduce cost estimates in future studies. [Copyright &y& Elsevier]

Monitoring emerging disinfection byproducts (DBPs) is challenging for many small water distribution networks (SWDNs), and machine learning-based predictive modeling could be an alternative solution. In this study, eleven machine learning techniques, including three multivariate linear regression-based, three regression tree-based, three neural networks-based, and two advanced non-parametric regression techniques, are used to develop models for predicting three emerging DBPs (dichloroacetonitrile, chloropicrin, and trichloropropanone) in SWDNs. Predictors of the models include commonly-measured water quality parameters and two conventional DBP groups. Sampling data of 141 cases were collected from eleven SWDNs in Canada, in which 70 % were randomly selected for model training and the rest were used for validation. The modeling process was reiterated 1000 times for each model. The results show that models developed using advanced regression techniques, including support vector regression and Gaussian process regression, exhibited the best prediction performance. Support vector regression models showed the highest prediction accuracy (R2 = 0.94) and stability for predicting dichloroacetonitrile and trichloropropanone, and Gaussian process regression models are optimal for predicting chloropicrin (R2 = 0.92). The difference is likely due to the much lower concentrations of chloropicrin than dichloroacetonitrile and trichloropropanone. Advanced non-parametric regression techniques, characterized by a probabilistic nature, were identified as most suitable for developing the predictive models, followed by neural network-based (e.g., generalized regression neural network), regression tree-based (e.g., random forest), and multivariate linear regression-based techniques. This study identifies promising machine learning techniques among many commonly-used alternatives for monitoring emerging DBPs in SWDNs under data constraints. [Display omitted] • Eleven machine learning techniques were compared for predicting emerging DBPs. • Support vector regression and Gaussian process regression are best-performing. • Support vector regression reduced 90 % error compared to linear regression. • Gaussian process regression reduced 67 % error compared to linear regression. • The two advanced regression techniques also showed the lowest prediction variance. [ABSTRACT FROM AUTHOR]

Oil spill incidents can significantly impact marine ecosystems in Arctic/subarctic areas. Low biodegradation rate, harsh environments, remoteness, and lack of sufficient response infrastructure make those cold waters more susceptible to the impacts of oil spills. A major challenge in Arctic/subarctic areas is to timely select suitable oil spill response methods (OSRMs), concerning the process complexity and insufficient data for decision analysis. In this study, we used various regression-based machine learning techniques, including artificial neural networks (ANNs), Gaussian process regression (GPR), and support vector regression, to develop decision-support models for OSRM selection. Using a small hypothetical oil spill dataset, the modelling performance was thoroughly compared to find techniques working well under data constraints. The regression-based machine learning models were also compared with integrated and optimized fuzzy decision trees models (OFDTs) previously developed by the authors. OFDTs and GPR outperformed other techniques considering prediction power (> 30 % accuracy enhancement). Also, the use of the Bayesian regularization algorithm enhanced the performance of ANNs by reducing their sensitivity to the size of the training dataset (e.g., 29 % accuracy enhancement compared to an unregularized ANN). [Display omitted] • Machine learning-based models are developed for effective response selection. • Models' performance is compared on a hypothetical oil spill dataset in the Arctic. • The best-performing models suited for small training datasets are selected. • Optimized fuzzy decision trees and Gaussian process regression models outperformed. • Recommendations are provided to prevent models' overtraining on small datasets. [ABSTRACT FROM AUTHOR]

This study presents a novel life cycle assessment-based framework for low-impact offshore oil spill response waste (OSRW) management. The framework consists of design of experiment, life cycle assessment (LCA), multi-criteria decision analysis (MCDA), operational cost analysis, and generation of regression models for impact prediction. The framework is applied to four OSRW management strategies as different combinations of solid and liquid oily waste collection, segregation, transportation, and treatment/disposal technologies. Hypothetical scenarios based on oily waste compositions are developed, and the associated environmental impacts and operational costs are evaluated. The LCA results show that oily waste composition accounts for < 5% of the total environmental impacts. Chemical demulsification has the highest total impacts due to high marine ecotoxicity and human toxicity, followed by incineration and transportation. The cost analysis reveals that the strategy comprised of centrifugation and landfilling is most preferable while the combination of chemical demulsification and incineration is least favorable. The strategy of combined use of centrifugation and landfilling is ranked as the most suitable in the MCDA. Regression models are developed to predict environmental impacts based on important factors. The framework can help waste management practitioners select low-impact strategies for handling offshore OSRW. [Display omitted] • An LCA-based framework is proposed for low-impact spill response waste management. • The strategies have relatively high marine ecotoxicity and human toxicity impacts. • The strategies are ranked by the lifecycle environmental impact and cost assessed. • The strategy of combined centrifugation and landfilling is the most suitable. • Multivariate regression models are developed for environmental impacts prediction. [ABSTRACT FROM AUTHOR]

Effective marine oil spill management (MOSM) is crucial to minimize the catastrophic impacts of oil spills. MOSM is a complex system affected by various factors, such as characteristics of spilled oil and environmental conditions. Oil spill detection, characterization, and monitoring; risk evaluation; response selection and process optimization; and waste management are the key components of MOSM demanding timely decision-making. Applying robust computational techniques based on real-time data (e.g., satellite and aerial observations) and historical records of oil spill incidents may considerably facilitate decision-making processes. Various soft-computing and artificial intelligence-based models and mathematical techniques have been used for the implementation of MOSM's components. This study presents a review of literature published since 2010 on the application of computational techniques in MOSM. A statistical evaluation is performed concerning the temporal distribution of papers, publishers' engagement, research subfields, countries of studies, and selected case studies. Key findings reported in the literature are summarized for two main practices in MOSM: spill detection, characterization, and monitoring; and spill management and response optimization. Potential gaps in applying computational techniques in MOSM have been identified, and a holistic computational-based framework has been suggested for effective MOSM. [Display omitted] • Oil spill response methods and waste management challenges are reviewed. • Applied intelligent techniques on remote sensing for spill detection are evaluated. • Machine learning and computational techniques used in oil spill risk analysis are studied. • Data mining and optimization approaches used in oil spill management are assessed. • A holistic computational-based framework is proposed for oil spill management. [ABSTRACT FROM AUTHOR]

Oil-based drill cuttings (OBDCs) were managed in two scenarios including low-temperature thermal desorption (LTTD) and secure landfill through a case study. The removal of polycyclic aromatic hydrocarbons (PAHs) and heavy metals in OBDCs by LTTD under different conditions was investigated. Probabilistic human health risk assessment was performed to quantify the health risk posed to waste management workers under the two scenarios, while the associated costs were also analyzed. The results show that LTTD at 300 °C for >20 min could remove 96.27% of PAHs in OBDCs but its removal effect on heavy metals was not significant. It was found that cancer risks posed by PAHs in both securely landfilled and LTTD-treated OBDCs were not significant (<1e-06); however, significant cancer risks (7.95e-05–9.45e-05) were identified for exposure to toxic heavy metals. Increased health risk was observed as a result of exposure to LTTD treatment residues compared to securely landfilled OBDCs. Inhalation of chromium(VI) and oral ingestion of arsenic in OBDCs were critical exposure routes. Both cancer and non-cancer risks in the secure landfill scenario were negligible. The cost analysis results suggest that LTTD combined with stabilization/solidification could be more economically attractive than secure landfill for the handling of OBDCs. [Display omitted] • Thermal desorption and secure landfill are designed for drill cuttings management. • Thermal desorption is effective for PAH removal but not effective for heavy metals. • Cancer risks posed by thermal desorption-treated drill cuttings are still significant. • Cancer and non-cancer risks posed by securely landfilled drill cuttings are negligible. • Thermal desorption has a lower cost than secure landfill because of resource recovery. [ABSTRACT FROM AUTHOR]

• Life cycle impacts of thermal desorption treatments of drill cuttings were assessed. • Thermomechanical cuttings cleaner generated the lowest impacts using hydropower. • Rotary drum dryer generated the lowest impacts in onsite drill cutting treatment. • Thermal desorption process contributed 80–90 % of the total impacts of systems. • Regression models were developed to help estimate impacts of different treatments. The life cycle impacts of treatment of typical oil-based drill cuttings (OBDCs) using three low-temperature thermal desorption (LTTD)-based systems, including thermomechanical cuttings cleaner (TCC), screw-type dryer (STD), and rotary drum dryer (RDD), were explored with a case study in British Columbia, Canada. Two energy supply scenarios, including diesel generator-based onsite (scenario i) and hydropower-based offsite (scenario ii) treatments, were considered in the assessment. The results show that RDD generated the lowest life cycle impacts in terms of damages to human health, ecosystems, and resources in scenario i. TCC-scenario ii generated the lowest impacts among all assessed cases, suggesting that using renewable energy can greatly reduce the impacts of LTTD-based OBDCs treatment. Also, net environmental benefits could be achieved considering the reuse of recovered oil, and the highest net environmental benefits were obtained in TCC-scenario ii. The process contribution analysis found that thermal desorption process accounted for 80–95 % of impacts in almost all impact categories. Energy consumption contours and linear regression models were also developed to help drilling waste managers estimate the life cycle impacts of using hydropower-driven TCC to treat OBDCs with different water and oil contents. [ABSTRACT FROM AUTHOR]