Corrosion is one of the main reasons for pipeline failure in the oil and gas industry. Because a pipeline failure can result in serious personal injury, monetary loss, and environmental damage, ...pipeline operators need to make timely, and cost-effective decisions to prevent accidents in high consequence areas. The majority of models proposed in this area are designed and computed for individual pipe segments, with only a limited number of studies considering the system level. The system level pertains to spatially distributed pipelines that span hundreds of miles across different environmental conditions. The current study propose combining GIS and Bayesian belief network to determine the probability of failure of transmission pipelines due to external pitting corrosion. The model incorporates data from an extensive network of pipes spanning hundreds of miles and their surroundings to compute the failure probability of the pipeline infrastructure. The combination of spatial GIS capabilities with the reasoning capabilities of the Bayesian network provides a powerful tool to estimate the likelihood of transmission pipelines failing in a specific area, based on the available information.
•A BBN and GIS integrated model is used to estimate a probability of pipeline failure.•Three discretization techniques are explored to compare the performance of the BBN.•The BBN model is used to perform a parametric study to identify critical parameters.
Abstract Public forest agencies are obligated to take steps to conserve and where possible enhance biodiversity, but they often lack information and tools that support and evidence their decision ...making. To help inform and monitor impact of management actions and policies aimed at improving forest biodiversity, we have co-developed a quantitative, transparent and repeatable approach for assessing the biodiversity potential of the United Kingdom’s (UK) publicly owned forests over space and time. The FOrest Biodiversity Index (FOBI) integrates several forest biodiversity indicators or ‘metrics’, which characterise management-sensitive woodland and landscape features associated with biodiversity. These are measured or modelled annually using spatially comprehensive forest survey data and other well-maintained spatial environmental datasets. Following metric normalisation and a correlation analysis, a statistically robust selection of these metrics is aggregated using a hierarchical procedure to provide composite index scores. The FOBI metric and index results are provided for every individual public forest, and can be summarised across any reporting region of interest. Compared to existing indicators that rely on sample-based forest data, the results thus better support decisions and obligations at a range of scales, from locally targeted action to national, long-term biodiversity monitoring and reporting. We set out how the FOBI approach and associated bespoke online interfaces were co-developed to meet public forest agency needs in two constituent countries of the UK (England and Scotland), whilst providing a conceptual framework that can be adapted and transferred to other geographic areas and private forests. Example results are reported for England’s public forests for four annual timestamps between 2014 and 2021, which indicate improvements to the biodiversity potential of public forests and surrounding landscapes over this time via increases in their diversity, extent, condition and connectivity.
Claims for ocean space are growing while marine ecosystems suffer from centuries of insufficient care. Human pressures from runoff, atmospheric emissions, marine pollution, fishing, shipping, ...military operations and other activities wear on habitats and populations. Ecosystem-based marine spatial planning (MSP) has emerged worldwide as a strategic instrument for handling conflicting spatial claims among competing sectors and the environment. The twofold objective of both boosting the blue economy and protecting the environment is challenging in practice and marine planners need decision support. Cumulative Impact Assessment (CIA) was originally developed to provide an overview of the human imprint on the world's ocean ecosystems. We have now added a scenario component to the CIA model and used it within Swedish ecosystem-based MSP. This has allowed us to project environmental impacts for different planning alternatives throughout the planning process, strengthening the integration of environmental considerations into strategic decision-making. Every MSP decision may entail a local shift of environmental impact, causing positive or negative consequences for ecosystem components. The results from Swedish MSP in the North Sea and Baltic Sea illustrate that MSP certainly has the potential to lower net cumulative environmental impact, both locally and across sea basins, as long as environmental values are rated high and prevailing pressures derive from activities that are part of MSP. By synthesizing innumerous data into comprehensible decision support that informs marine planners of the likely environmental consequences of different options, CIA enables ecosystem-based MSP in practice.
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•Cumulative impact assessment was integrated with marine spatial planning•Existing methods were enhanced by developing functions for scenario analyses•Environmental consequences of different planning options were compared•Implementing mm marine spatial plans likely to reduce cumulative impact in the Swedish North Sea•Demonstrated tool supports ecosystem based marine spatial planning in practice
Traditional wastewater-based epidemiology (W-BE) relying on SARS-CoV-2 RNA detection in wastewater is attractive for understanding COVID-19. Yet traditional W-BE based on centralized wastewaters ...excludes putative SARS-CoV-2 reservoirs such as: (i) wastewaters from shared on-site sanitation facilities, (ii) solid waste including faecal sludge from non-flushing on-site sanitation systems, and COVID-19 personal protective equipment (PPE), (iii) raw/untreated water, and (iv) drinking water supply systems in low-income countries (LICs). A novel hypothesis and decision-support tool based on Wastewater (on-site sanitation, municipal sewer systems), solid Waste, and raw/untreated and drinking Water-based epidemiology (WWW-BE) is proposed for understanding COVID-19 in LICs. The WWW-BE conceptual framework, including components and principles is presented. Evidence on the presence of SARS-CoV-2 and its proxies in wastewaters, solid materials/waste (papers, metals, fabric, plastics), and raw/untreated surface water, groundwater and drinking water is discussed. Taken together, wastewaters from municipal sewer and on-site sanitation systems, solid waste such as faecal sludge and COVID-19 PPE, raw/untreated surface water and groundwater, and drinking water systems in LICs act as potential reservoirs that receive and harbour SARS-CoV-2, and then transmit it to humans. Hence, WWW-BE could serve a dual function in estimating the prevalence and potential transmission of COVID-19. Several applications of WWW-BE as a hypothesis and decision support tool in LICs are discussed. WWW-BE aggregates data from various infected persons in a spatial unit, hence, putatively requires less resources (analytical kits, personnel) than individual diagnostic testing, making it an ideal decision-support tool for LICs. The novelty, and a critique of WWW-BE versus traditional W-BE are presented. Potential challenges of WWW-BE include: (i) biohazards and biosafety risks, (ii) lack of expertise, analytical equipment, and accredited laboratories, and (iii) high uncertainties in estimates of COVID-19 cases. Future perspectives and research directions including key knowledge gaps and the application of novel and emerging technologies in WWW-BE are discussed.
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•A novel wastewater, waste, and water-based epidemiology (WWW-BE) is postulated.•The rationale and principles of WWW-BE in low-income countries (LICs) are discussed.•WWW-BE may unravel the distribution, burden and transmission of COVID-19 in LICs.•WWW-BE is a novel decision-support tool for targeting resources and control methods.•Biosafety risks, lack of skills and analytical kits could limit the use of WWW-BE in LICs.
Patients in cognitive behavioural therapy (CBT) who are high in interpersonal sensitivity may have difficulty fully engaging in treatment because therapy sessions require intimate interpersonal ...interactions that are especially uncomfortable for these individuals. The current study tests the hypotheses that patients who are high in interpersonal sensitivity benefit less from CBT for symptoms of depression and anxiety, show a slower rate of change in those symptoms, and are more likely to drop out of treatment.
Participants were 832 outpatients who received naturalistic CBT. We assessed interpersonal sensitivity before treatment began and depression and anxiety symptoms at every therapy session. We assessed early, premature, and uncollaborative termination after treatment ended. We constructed multilevel linear regression models and logistic regression models to assess the effects of baseline interpersonal sensitivity on the treatment outcome, the slope of change in depression and anxiety symptoms, and each type of dropout.
Higher baseline interpersonal sensitivity was associated with a slower rate of change and less overall change in anxiety but not depressive symptoms. Baseline interpersonal sensitivity was not a predictor of dropout.
Interpersonal sensitivity at baseline predicts less change and a slower rate of change in anxiety symptoms. Early detection of elevated interpersonal sensitivity can help therapists take action to address these barriers to successful treatment and help scientists build decision support tools that accurately predict the trajectory of change in anxiety symptoms for these patients.
Policymakers are in a balancing act when creating local energy transition strategies. Embedding new technologies in an existing energy system is highly complex. Policymakers must deal with ...multi-system interactions such as sector coupling, multi-scale effects such as bottom-up behavior and top-down policies, and requirements from local spatial planning, grid constraints, and resource availability.
Decision support tools can help to navigate this complex landscape. This paper showcases a tool to support policymakers with heating strategies for Dutch neighborhoods. The tool is a GIS-based simulation model of the energy system developed using a collaborative approach and applied in a scenario study. Energy calculations are done over a year with an hourly resolution, while scenarios can include any future energy system configuration. The results highlight trade-offs between heating strategies, interaction effects with the mobility and electricity transitions, and bottlenecks in transition pathways. Collective district heating has less grid impact but higher emissions and costs, while individual (hybrid) heat pumps have lower emissions and costs but more grid impact. No-regrets and enabling technologies are insulation and smart charging of electric vehicles and boilers.
Collaborative modeling with a GIS-based user-interface increases system understanding, including trade-offs, transition pathways, and bottlenecks, in a collective and interactive way. This creates a shared and well-grounded vision, resulting in robust local renewable energy strategies.
•A multi-system, multi-scale decision support tool is an essential contribution to local renewable energy system planning.•There is no clear winner in renewable heating strategies in residential neighborhoods in The Netherlands.•Trade-offs occur in terms of costs, grid congestion, and emission reductions.•Models can be used to identify lock-ins and bottlenecks in energy transition pathways.•Collaborative modeling for renewable energy strategies increases system understanding and more robust decision-making.
Human activities are recognised as primarily responsible for global warming, with consequences such as biodiversity loss and extreme weather phenomena that impact human well-being. This study ...proposes a methodological approach for numerical simulation of the interactions between climate phenomena, the built environment, and the individual, based on the assumption that thoroughly understanding these connections will enable the development of more effective intervention strategies for climate mitigation and human comfort. The methodology is applied to three pilot cases in order to analyse the influence of different parameters of the urban built environment on physiological stress: the Quarticciolo neighbourhood (former borough) in Rome (Italy), the Westside San Antonio neighbourhood in Texas (USA), and the Shuangta Suzhou neighbourhood in China. Article info Received: 18/03/2024; Revised: 10/04/2024; Accepted: 19/04/2024
This article introduces a new web-based decision support system created for early-stage feasibility assessments of renewable energy technologies in England, UK. The article includes a review of ...energy policy and regulation in England and a critical evaluation of literature on similar decision support systems. Overall, it shows a novel solution for a repeatable, scalable digital evidence base for the policy compliant deployment of renewable energy technologies.
Data4Sustain is a spatial decision support system developed to quickly identify the feasibility of seven renewable energy technologies across large areas including wind, solar, hydro, shallow and geothermal. A multi-actor approach was used to identify the key factors that influence the technical feasibility of these technologies to generate electricity or heat for local consumption or regional distribution. The research demonstrates opportunities to improve the links between policy and regulation with deployment of renewable energy technologies using novel approaches to digital planning.
Deployed, resilient, cost-effective and societally accepted renewable energy generation infrastructure has a role to play in ensuring universal access to affordable, reliableand modern energy supply. This is central to supporting a concerted transition to a low-carbon future in order to address climate change. The selection and siting of renewable energy technology is driven by natural resource availability and physical and regulatory constraints. These factors inform early-stage feasibility of renewables, helping to focus investment of time and money. Understanding their relative importance and identifying the most suitable technologies is a highly complex task due to the disparate and often unconnected sources of data and information needed. Data4Sustain help to overcome these challenges.
•Policy is designed for energy production and to protect against climate change.•Tool providing digital evidence base de-centralised energy.•Co-developed with experts to output spatial feasibility of seven renewables.•Supports early-stage implementation of energy and planning policies.
In recent years, interest on sustainable supply chain management (SSCM) has risen significantly in both the academic and business communities. This is confirmed by the growing number of conferences, ...journal publications, special issues and websites dedicated to the topic. Within this context, this paper reviews the existing literature related to decision-support tools and performance measurement for SSCM. A narrative literature review is carried out to capture qualitative evidence, while a systematic literature review is performed using classic bibliometric techniques to analyse the relevant body of knowledge identified in 384 papers published from 2000 to 2013. The key conclusions include: the evidence of a research field that is growing, the call for establishing the scope of current research, i.e. the need for integrated performance frameworks with new generation decision-support tools incorporating triple bottom line (TBL) approach for managing sustainable supply chains. There is a need to identify a wide range of specific industry-related TBL metrics and indexes, and assess their usefulness through empirical research and case-base analysis. We need mixed methods to thoroughly analyse and investigate sustainable aspects of the product life cycle across the supply chains, through empirical evidence, building and/or testing theory from and in practice.
In sub-Saharan Africa (SSA), rice production from smallholder farms is challenged because of a lack of fertilizer inputs and nutrient-poor soils. Therefore, improving nutrient efficiency is ...particularly important for increasing both fertilizer use and rice yield. This review discusses how to improve the return from fertilizer input in terms of agronomic N use efficiency (AE
N
), that is, the increase in grain yield per kg of applied N, for rice production in SSA. The AE
N
values we summarized here revealed large spatial variations even within small areas and a certain gap between researcher-led trials and smallholder-managed farms. Experimental results suggest AE
N
can be improved by addressing spatial variations in soil-related factors such as P, S, Zn, and Si deficiencies and Fe toxicity in both irrigated and rainfed production systems. In rainfed production systems, differences in small-scale topography are also important which affects AE
N
through dynamic changes in hydrology and variations in the contents of soil organic carbon and clay. Although empirical evidence is further needed regarding the relationship between soil properties and responses to fertilizer inputs, recent agricultural advances have generated opportunities for integrating these micro-topographical and soil-related variables into field-specific fertilizer management. These opportunities include UAV (unmanned aerial vehicle) technology to capture microtopography at low cost, database on soil nutrient characteristics at high resolution and more numbers of fertilizer blending facilities across SSA, and interactive decision support tools by use of smartphones on site. Small-dose nursery fertilization can be also alternative approach for improving AE
N
in adverse field conditions in SSA.
ABBREVIATIONS: AE
N
: agronomic nitrogen use efficiency; FISP: farm input subsidy program; VCR: value cost ratio; SOC: soil organic carbon; SSA: sub-Saharan Africa; UAV: unmanned aerial vehicle
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