Risk Mitigation Research Articles

Risk Allocation and Mitigation Practices for Building Information Modeling: Addressing Legal and Contractual Risks Associated with Contract Documentation

Risk Allocation and Mitigation Practices for Building Information Modeling: Addressing Legal and Contractual Risks Associated with Contract Documentation

Planning scale flood risk assessment and prediction in ultra-high density urban environments: The case of Hong Kong

Climate change has significantly increased the risks associated with urban flooding. However, most research on flood risk assessment focuses on large-scale climate changes and impacts, leaving a research gap in the high spatial resolution of flood risk assessment in inter-urban areas. This gap makes it difficult to guide regional planning for the government. Therefore, this study aims to explore the risks of floods in ultra-high-density cities under climate change at the planning scale, using Hong Kong as a case study. We comprehensively assessed the flood risk index (FRI) in the built environment in 211 tertiary planning units (TPUs) from the three dimensions of vulnerability, exposure, and hazard from 2006 to 2021. We also employed a prediction model to forecast the spatial–temporal patterns of FRI in the next 5, 10, and 15 years and evaluated the uneven distribution of flood risks. The results show that the FRI of TPUs increased yearly, which poses higher threats to agglomerative areas of transportation and functional facilities. Additionally, future FRI will further impact coastal TPUs in western Hong Kong, resulting in more negative impacts on high-building areas. Therefore, urban planning should prioritize integrating flood management and risk mitigation measures.

Sustainable supply chain financing models: Integrating banking for enhanced sustainability

Sustainable supply chain financing has emerged as a pivotal strategy in contemporary business paradigms, aiming to harmonize economic growth with environmental stewardship and social responsibility. This review delves into the integration of banking mechanisms within sustainable supply chain financing models to foster enhanced sustainability across diverse industries. In recent years, the concept of sustainability has gained momentum, prompting organizations to reevaluate their operational frameworks to mitigate environmental impacts and address societal concerns. Supply chain financing, a financial tool facilitating transactions among interconnected entities within the supply chain, plays a crucial role in advancing sustainability objectives. By integrating banking institutions into these models, businesses can leverage financial expertise, resources, and networks to bolster sustainable practices throughout the supply chain. This review explores various dimensions of sustainable supply chain financing models, highlighting the significance of banking integration. Firstly, it elucidates the evolving landscape of sustainability in supply chains, emphasizing the imperative for concerted efforts to reconcile profitability with environmental preservation and social welfare. Secondly, it examines the multifaceted benefits of integrating banking institutions into supply chain financing mechanisms, including access to capital, risk mitigation, and expertise in sustainable finance. Moreover, the review discusses the emergence of innovative financial instruments such as green bonds, sustainability-linked loans, and supply chain finance programs tailored to promote sustainability goals. These instruments not only provide financial incentives for sustainable initiatives but also foster transparency and accountability throughout the supply chain ecosystem. Furthermore, the review addresses challenges and opportunities associated with integrating banking into sustainable supply chain financing, including regulatory complexities, technological advancements, and stakeholder collaboration. It underscores the need for strategic partnerships between businesses, financial institutions, and regulatory bodies to navigate these challenges effectively and realize the full potential of sustainable supply chain financing. The integration of banking institutions into sustainable supply chain financing models presents a promising avenue for advancing sustainability agendas across industries. By harnessing financial innovation and collaboration, organizations can foster resilience, efficiency, and ethical conduct within their supply chains, contributing to a more sustainable and inclusive global economy.

Dominant risk factors (DRFs) in construction-specific supply chains: a systematic review

PurposeDue to the uniqueness of individual construction projects, identifying the dominant risk factors is needed for risk mitigation in ongoing and future projects. This study aims to identify the dominant construction supply chain risk (CSCR) factors, based on studies conducted between 2002 and 2022.Design/methodology/approachThe study adopts the preferred reporting items for systematic reviews and meta-analysis (PRISMA) procedure to identify, screen and select relevant articles in order to provide a bibliography and annotation of the prevalent risks in the supply chains. A descriptive analysis of the findings then follows.FindingsThe study’s findings have highlighted the three most prevalent risks in the construction supply chain (poor communication across project teams, changes in foreign currency rate, unfavorable climate conditions) as reported in literature, that project teams need to pay closer attention to and take proactive steps to mitigate.Research limitations/implicationsDue to limitations imposed by the chosen research methodology, tools, time frame and article availability, the study was unable to examine all CSCR-related papers.Practical implicationsThe results will serve as a useful roadmap for risk/supply chain managers in the construction industry to take strategically proactive steps towards allocating resources for CSCR mitigation efforts.Social implicationsContext-specific research on the impact of social and cultural risks on the construction supply chain would be beneficial, due to emerging social network risk factors and the complex socio-cultural settings.Originality/valueThere is presently no study that has reviewed extant studies to identify and compile the dominant risk factors (DRFs) associated with the supply chain of construction projects for ranking in the supply chain risk management process.

Management of Acute Wounds - Expert Panel Consensus Statement.

The Wound Healing Foundation recognized the need for consensus-based unbiased recommendations for the treatment of wounds. As a first step, a consensus on the treatment of chronic wounds was developed and published in 2022.(1) The current publication on acute wounds represents the second step in this process. Acute wounds may result from any number of conditions, including burns, military and combat operations, and trauma to specific areas of the body. The management of acute wounds requires timely and evidence-driven intervention to achieve optimal clinical outcomes. This consensus statement provides the clinician with the necessary foundational approaches to the causes, diagnosis and therapeutic management of acute wounds. Presented in a structured format, this is a useful guide for clinicians and learners in all patient care settings. Recent advances in the management of acute wounds have centered on stabilization and treatment in the military and combat environment, Specifically advancements in hemostasis, resuscitation, and the mitigation of infection risk through timely initiation of antibiotics and avoidance of high pressure irrigation in contaminated soft tissue injury. . Critical issues include infection control, pain management and the unique considerations for the management of acute wounds in pediatric patients. Future directions include new approaches to preventing the progression and conversion of burns through the use of the microcapillary gel, a topical gel embedded with the anti-inflammatory drug infliximab.(38) Additionally, the use of three-dimensional bioprinting and photo-modulation for skin reconstruction following burns is a promising area for continued discovery.

Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes

In hospital settings, effective risk management is critical to ensuring patient safety, regulatory compliance, and operational effectiveness. Conventional approaches to risk assessment and mitigation frequently rely on manual procedures and retroactive analysis, which might not be sufficient to recognize and respond to new risks as they arise. This study examines how artificial intelligence (AI) technologies can improve risk management procedures in healthcare facilities, fortifying patient safety precautions and guidelines while improving the standard of care overall. Hospitals can proactively identify and mitigate risks, optimize resource allocation, and improve clinical outcomes by utilizing AI-driven predictive analytics, natural language processing, and machine learning algorithms. The different applications of AI in risk management are discussed in this paper, along with opportunities, problems, and suggestions for their effective use in hospital settings.

Land subsidence monitoring using InSAR technique in the southwestern region of Bangladesh

Deltas like the Ganges-Brahmaputra Delta (GBD) experience significant land subsidence due to overlaying load, consolidation, sediment compaction, regional tectonics, and human activity. The southwestern region of Bangladesh, marked by its low-lying floodplain with compressible sediments, experiences significant land subsidence. However, accurate assessments for these areas are very scarce, often focusing on the point-based measurements. This study employed Interferometric Synthetic Aperture Radar analysis, spanning from 2014 to 2022, to effectively measure land subsidence, and visualize it spatially. In the study area, subsidence rates were found to be ranging from 3 to 20 mm/year, with most areas experiencing 3-10 mm/year. While some areas show higher rates of 10-13 mm/year, very few exceeded 14 mm/year. The average subsidence rate was 5.98 mm/year, demonstrating significant spatial variability, with higher rates in Gopalganj, Patuakhali, Bhola, Barishal, and other areas. The findings were validated based on existing Global navigation satellite system (GNSS) data points, affirming their reliability. Despite minor discrepancies with other measurements at specific points, the overall results align well with existing data. This comprehensive analysis provides a detailed assessment of land subsidence in the southwestern region of Bangladesh, highlighting significant spatial variability and offering valuable insights for future coastal development and risk mitigation strategies.

Enhancing Safety in Cargo Handling Units Using Thermal and Motion IoT Sensors

To improve supply chain efficiency by establishing real-time communication via Internet of Things (IoT) devices between cargo units and Logistics Control Systems (LCS). The method makes it possible to continuously monitor characteristics like location, temperature, humidity, and handling affects by carefully placing IoT sensors within cargo containers. These Internet of Things (IoT) devices work together to create a network that sends data in real time to a centralized LCS platform. This data gives logistics operators fast insights for proactive disruption response, risk mitigation, and optimized route planning. The research uses encryption techniques, redundancy plans, and flexible software interfaces to address issues with data security, device dependability, and integration into current infrastructure. Furthermore, by integrating machine learning and predictive analytics, the LCS can anticipate potential problems, which helps promotes increased customer happiness, decreased losses, and greater operational performance in contemporary logistics and freight transportation.

Building Engineering Cost Prediction Based On Deep Learning: Model Construction and Real - Time Optimization

Effective project planning, risk mitigation, and stakeholder satisfaction in the construction business are greatly impacted by accurate cost projection. Overspending, setbacks, and ruined projects are all possible results of imprecise cost estimates. For this reason, it is critical to guarantee the viability and success of a project by increasing the precision of cost predictions. Construction project complexity, a myriad of cost variables, and uncertainty are the obstacles that building engineering cost prediction must overcome. Predictions made using traditional approaches are commonly inaccurate because they fail to fully account for the complex interplay between project factors and expenses. Advanced modelling techniques that can handle complicated data and changeable project contexts are necessary to overcome these obstacles. An approach based on deep learning called Deep CostNet for Building Engineering Technique (DCN-BET) Cost Prediction is presented in this research. Its purpose is to solve the problems associated with building engineering cost prediction. The approach uses deep neural networks to extract intricate patterns from massive amounts of project data collected over time. Improved prediction accuracy and real-time optimisation during project execution are made possible by DCN-BET, which captures the nonlinear correlations between project characteristics and costs. Risk assessment and management, cost forecasting for resource allocation, and project budget estimation and planning are among the few of the many construction industry uses for DCN-BET. The effectiveness of DCN-BET is assessed by conducting thorough simulation analyses in contrast to more conventional cost prediction approaches. Training and testing the model with real-world building engineering datasets allows us to evaluate its accuracy and efficacy in project cost prediction. The results show that DCN-BET has the capacity to support real-time optimisation and significantly improved the accuracy of cost predictions, which improved the overall success and efficiency of the project. 

Construction industry changes induced by the COVID-19 pandemic

PurposeThe COVID-19 pandemic caused significant societal changes and altered how much of the construction industry operates. This study investigates the impacts of pandemic-related changes, how these changes may apply to different companies, and which changes should continue post-pandemic.Design/methodology/approachWe aim to identify pandemic-driven changes that have affected the construction workplace and the advantages and challenges associated with them. We then make recommendations for what could and should endure through the pandemic and beyond, and under what circumstances. To achieve this objective, we conducted both qualitative and quantitative analyses of 40 semi-structured interviews with US-based construction professionals.FindingsIdentified through these interviews were 21 pandemic-driven changes across six categories: management and planning, technology, workforce, health and safety, supply chain, and contracts. This study noted both positive and negative impacts of the changes on cost, schedule, productivity, collaboration, employee retention, flexibility, quality, and risk mitigation. Participants indicated that some changes should remain after the pandemic and others (e.g. select safety measures, schedule adjustments) should be temporary.Originality/valueBy incorporating these lessons learned into recommendations, the findings of this study will help businesses identify and implement the most appropriate improvements for their organizations. The findings also provide policymakers with valuable insights on how to promote innovation in the construction industry and potentially enact more effective policies during crises to drive long-term improvements.

Site-specific risk-based threshold (RBT) values for sewage-associated markers in estuarine bathing waters

This study establishes site-specific risk-based thresholds (RBTs) for sewage-associated markers, including Bacteroides HF183 (HF183), Lachnospiraceae Lachno3 (Lachno3), cross-assembly phage (CrAssphage), and pepper mild mottle virus (PMMoV), utilizing quantitative microbial risk assessment (QMRA) for recreational estuarine waters (EW). The QMRA model simulates health risks from ingestion of EW contaminated with untreated sewage. RBTs were estimated considering site-specific decay rates and concentrations of markers and pathogen (human norovirus; HNoV), aiding in the identification of high-risk days during the bathing season. Results indicate varying RBT concentrations for fresh (Day 0) and aged (Days 1 to 10) sewage contamination scenarios over 10 days. HF183 exhibited the highest RBT (26,600 GC/100 mL) initially but decreased rapidly with aging (2570 to 3120 GC/100 mL on Day 10), while Lachno3 and CrAssphage remained relatively stable. PMMoV, despite lower initial concentrations (3920 GC/100 mL), exhibited increased RBT (4700 to 6440 GC/100 mL) with aging due to its slower decay rate. Sensitivity analysis revealed HNoV concentrations as the most influential parameter. Comparison of marker concentrations in estuarine locations with median RBTs showed instances of marker exceedance, suggesting potential health risks. The observed discrepancies between bacterial and viral marker concentrations in EW highlight the need for optimized sample concentration method and simultaneous measurement of multiple markers for enhanced risk predictions. Future research will explore the utility of multiple markers in risk management. Overall, this study contributes to better understanding human health risks in recreational waters, aiding regulators, and water quality managers in effective decision-making for risk prioritization and mitigation strategies.

Clonal Hematopoiesis and Inflammation in the VasculaturE (CHIVE): a prospective, longitudinal clonal hematopoiesis cohort and biorepository

Clonal hematopoiesis (CH) is an age-associated phenomenon leading to increased risk of both hematologic malignancy and non-malignant organ dysfunction. Increasingly available genetic testing has made incidental discovery of CH clinically common, yet evidence-based guidelines and effective management strategies to prevent adverse CH health outcomes are lacking. To address this gap, the prospective CHIVE registry and biorepository was created to identify and monitor individuals at risk, support multidisciplinary CH clinics, and to refine standards of practice for CH risk mitigation. Data from the first 181 patients enrolled in this registry recapitulate the molecular epidemiology of CH from biobank scale retrospective studies, with DNMT3A, TET2, ASXL1, and TP53 as the most commonly mutated genes. CH patients had higher rates of end organ dysfunction, in particular chronic kidney disease (p=0.001). Among patients with CH, variant allele frequency was independently associated with presence of cytopenias (p=0.008) and progression to hematologic malignancy (p=0.010), while other common high-risk CH clone features were not clear. Notably, accumulation of multiple distinct high-risk clone features was also associated with cytopenias (p=0.013) and hematologic malignancy progression (p=0.004), supporting a recently published CH risk score. Surprisingly, ~30% of patients enrolled in CHIVE from CH clinics were adjudicated as not having CHIP, highlighting the need for molecular standards and purpose-built assays in this field. Maintenance of this well-annotated cohort and continued expansion of CHIVE to multiple institutions is underway and will be critical to understand how to thoughtfully care for this patient population.

Soil Geobacteraceae are the key predictors of neurotoxic methylmercury bioaccumulation in rice.

Contamination of rice by the potent neurotoxin methylmercury (MeHg) originates from microbe-mediated Hg methylation in soils. However, the high diversity of Hg methylating microorganisms in soils hinders the prediction of MeHg formation and challenges the mitigation of MeHg bioaccumulation via regulating soil microbiomes. Here we explored the roles of various cropland microbial communities in MeHg formation in the potentials leading to MeHg accumulation in rice and reveal that Geobacteraceae are the key predictors of MeHg bioaccumulation in paddy soil systems. We characterized Hg methylating microorganisms from 67 cropland ecosystems across 3,600 latitudinal kilometres. The simulations of a rice-paddy biogeochemical model show that MeHg accumulation in rice is 1.3-1.7-fold more sensitive to changes in the relative abundance of Geobacteraceae compared to Hg input, which is recognized as the primary parameter in controlling MeHg exposure. These findings open up a window to predict MeHg formation and accumulation in human food webs, enabling more efficient mitigation of risks to human health through regulations of key soil microbiomes.

Artificial Intelligence Approach in Aerospace for Error Mitigation

Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation.

Navigating Uncertainties: A Holistic Framework for Identifying and Assessing Risks in Third-Party Logistics (3PL)

To review the extant literature on 3PL risk management (SCRM, including risk identification, assessment, treatment, and monitoring), developing a comprehensive definition and conceptual framework; to evaluate prior theory use; and to identify future research directions. Additionally, our framework emphasizes the importance of aligning performance metrics with risk objectives, facilitating ongoing monitoring and evaluation of 3PL partnerships. By establishing clear performance benchmarks and feedback mechanisms, organizations can promptly identify emerging risks and implement corrective actions as necessary. Methodology: A systematic literature review of couple of articles (published 2000-2016) based on descriptive, thematic, and content analysis. Drawing from extensive literature review and industry best practices, our framework integrates multiple dimensions of risk identification and evaluation into a cohesive methodology. We propose a structured approach that encompasses both qualitative and quantitative techniques, enabling organizations to comprehensively assess risks across various stages of the 3PL relationship lifecycle. Through the application of our holistic framework, organizations can enhance their ability to navigate uncertainties in 3PL relationships, fostering resilience and competitiveness in today's dynamic business environment. Findings: There has been a considerable focus on identifying risk types and proposing risk mitigation strategies. Research has emphasised organisational responses to 3PL risks. Research implications: A broad, contemporary understanding of 3PL risk management is provided; leading to a conceptual framework. The research agenda guides future work towards maturation of the discipline. Practical implications: Managers are encouraged to adopt a holistic approach to risk management. Guidance is provided on how to select appropriate risk treatment actions according to the probability and impact of a risk. Key components of our framework include risk categorization, stakeholder engagement, scenario analysis, and performance metrics alignment. By systematically categorizing risks according to their nature and impact, organizations can prioritize mitigation efforts and allocate resources effectively. Stakeholder engagement ensures that diverse perspectives are considered in the risk assessment process, enhancing its accuracy and relevance. Furthermore, scenario analysis allows for the exploration of potential future events and their implications on 3PL operations, enabling proactive risk management strategies. Keywords: 3PL, Risk management, Systematic literature review (SLR), theoretical approach

Worldviews more than experience predict Californians’ support for wildfire risk mitigation policies

Abstract California must adapt to increasing wildfire activity concurrent with climate change and expanding housing development in fire-prone areas. Recent decades have seen record-breaking fire activity, economic costs, and human health impacts. Residents more frequently face home evacuations, prolonged periods of unhealthy air quality, and power shut-offs. Understanding how these experiences influence support for risk mitigation policies is essential to inform action on climate and fire adaptation. To better understand linkages between experience and policy support, we surveyed California residents (n = 645) about their wildfire-related experiences, risk perceptions, and support for 18 wildfire risk mitigation policies. To assess how the relationship between policy support and wildfire experience is modulated by preexisting worldviews, we measured the extent to which respondents are motivated by individualistic or communitarian values as proposed in the cultural theory of risk. We surveyed residents across a gradient of wildfire impacts, spatially stratifying residences based on wildland-urban-interface (WUI) type and proximity to large 2020 wildfires. Support was generally high for most policies, though most respondents opposed incorporating future risk into insurance rates and coverage. Policy support models showed that communitarian worldviews were more consistently associated with greater support for diverse wildfire mitigation policies than were measures of recent experience with wildfire. These results suggest that California residents within our sample regions already support many wildfire risk mitigation strategies, and preexisting societal beliefs are a stronger predictor of these views than personal experiences with wildfire. Policy-makers can utilize this understanding to focus on crafting policies and messaging that resonates with individualistic values.

CACS, CCTA and mCAD-LT score in the pre-transplant assessment of coronary artery disease and the prediction of post-transplant cardiovascular events.

The optimal cardiovascular assessment of liver transplant (LT) candidates is unclear. We aimed to evaluate the performance of CT-based coronary tests (coronary artery calcium score [CACS] and coronary CT angiography [CCTA]) and a modification of the CAD-LT score (mCAD-LT, excluding family history of CAD) to diagnose significant coronary artery disease (CAD) before LT and predict the incidence of post-LT cardiovascular events (CVE). We retrospectively analysed a single-centre cohort of LT candidates who underwent non-invasive tests; invasive coronary angiography (ICA) was performed depending on the results of non-invasive tests. mCAD-LT was calculated in all patients. Six-hundred-and-thirty-four LT candidates were assessed and 351 of them underwent LT. CACS, CCTA and ICA were performed in 245, 123 and 120 LT candidates, respectively. Significant CAD was found in 30% of patients undergoing ICA. The AUROCs of mCAD-LT (.722) and CCTA (.654) were significantly higher than that of CACS (.502) to predict the presence of significant CAD. Specificity of the tests ranged between 31% for CCTA and 53% for CACS. Among patients who underwent LT, CACS ≥ 400 and mCAD-LT were independently associated with the incidence of CVE; in patients who underwent CCTA before LT, significant CAD at CCTA also predicted post-LT CVE. In this cohort, mCAD-LT score and CT-based tests detect the presence of significant CAD in LT candidates, although they tend to overestimate it. Both mCAD-LT score and CT-based tests classify LT recipients according to their risk of post-LT CVE and can be used to improve post-LT risk mitigation.

Contemporary Risk Models for In-Hospital and 30-Day Mortality After Percutaneous Coronary Intervention.

Risk models for mortality after percutaneous coronary intervention (PCI) are underutilized in clinical practice though they may be useful during informed consent, risk mitigation planning, and risk adjustment of hospital and operator outcomes. This review analyzed contemporary risk models for in-hospital and 30-day mortality after PCI. We reviewed eight contemporary risk models. Age, sex, hemodynamic status, acute coronary syndrome type, heart failure, and kidney disease were consistently found to be independent risk factors for mortality. These models provided good discrimination (C-statistic 0.85-0.95) for both pre-catheterization and comprehensive risk models that included anatomic variables. There are several excellent models for PCI mortality risk prediction. Choice of the model will depend on the use case and population, though the CathPCI model should be the default for in-hospital mortality risk prediction in the United States. Future interventions should focus on the integration of risk prediction into clinical care.

A Study on Agricultural Loans offered by NABARD to Farmers

This study delves into the comprehensive landscape of agricultural loans disbursed by the National Bank for Agriculture and Rural Development (NABARD) to farmers in India. The research scrutinizes the varied purposes for which these loans are extended, encompassing crop cultivation, animal husbandry, dairy farming, poultry, fisheries, and agro-processing. Emphasizing the different loan types – short-term, medium-term, and long-term – the investigation explores the interest rates associated with each category and evaluates the role of government subsidies in enhancing affordability. Additionally, the study investigates the credit delivery mechanism facilitated by NABARD through cooperative banks, regional rural banks, and commercial banks, ensuring an inclusive approach to reach farmers across diverse strata. The research underscores the technological impetus provided by NABARD, encouraging farmers to adopt modern agricultural practices and technologies. Special attention is given to risk mitigation measures, including crop insurance, designed to shield farmers from losses arising due to unforeseen circumstances. Keywords: NABARD, Agricultural Loans, Farmer Financing, Credit Delivery, Government Subsidies, Rural Development, Risk Mitigation, Financial Inclusion, Technological Adoption, Indian Agriculture.

Present Status of Microplastic Pollution Research Data in Sri Lanka and Microplastic Risk Mitigation Solutions; Lessons from a Global Policy Context

The emergence of microplastics (MP) as a pollutant in natural environments including aquatics has been increasingly recognized worldwide. This review focuses on the status of MP pollution research in Sri Lanka, and MP risk mitigation solutions, as lessons from a global MP policy context. The methodology involves a comprehensive literature review divided into three main sections: 1) a simple understanding of the plastic cycle and risk factors,2) a comprehensive review of MP pollution research trends in Sri Lanka, 3) a comprehension of global trends of MP regulation policies and adaptable solutions for national scale. There was less attention given to MP research in Sri Lanka, until the recent X-Press Pearl disastrous incident. In addition to that, we highlight the less attention paid to MP pollution in inland waters and lands compared to marine. Considering the widespread MP issue, the paper highlights the importance of a policy approach for MP pollution control. Finally, the paper discusses the future directions for MP pollution research in Sri Lanka and emphasizes the need for more detailed quantitative data for effective policy formulation. The overall study presents a sound case for understanding a national context in MP pollution and suggesting necessary policy instruments in pollution regulation.