Toward a generic analytical framework for sustainable nitrogen management: application for China

Managing reactive nitrogen (Nr) to achieve a sustainable balance between production of food, feed and fiber, and environmental protection is a grand challenge in the context of an increasingly affluent society. Here, we propose a novel framework for national nitrogen (N) assessments enabling a more consistent comparison of the uses, losses and impacts of Nr between countries, and improvement of Nr management for sustainable development at national and regional scales. This framework includes four key components: national scale N budgets, validation of N fluxes, cost-benefit analysis and Nr management strategies. We identify four critical factors for Nr management to achieve the sustainable development goals: N use efficiency (NUE), Nr recycling ratio (e.g., ratio of livestock excretion applied to cropland), human dietary patterns and food waste ratio. This framework was partly adopted from the European Nitrogen Assessment and now is successfully applied to China, where it contributed to trigger policy interventions toward improvements for future sustainable use of Nr. We demonstrate how other countries can also benefit from the application our framework, in order to include sustainable Nr management under future challenges of growing population, hence contributing to the achievement of some key sustainable development goals (SDGs)

MODELLING FRAMEWORK FOR CRITICAL SUCCESS FACTORS OF GREEN SUPPLY CHAIN MANAGEMENT-AN INTEGRATED APPROACH OF PARETO, ISM AND SEM

The study aimed in identifying Green supply chain critical success factors, develop and validate the framework through integrated approach of ISM, MICMAC and SEM so as to promote green practices throughout the supply chain activities in Indian manufacturing sectors. Interpretive structural modelling(ISM) is applied to develop hierarchical contextual relationship among identified critical success factors via Pareto analysis. The methodology then follows classification of success factors into four clusters by Matrice d’ Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC) and statistical validation of the ISM model through Structural Equation Modelling(SEM) by AMOS. In this study, 16 critical success factors of Green supply chain practices for manufacturing industries were identified, followed by development of an ISM model using 16 critical success factors, later the model was statistically verified that identified nine CSF’s responsible for generating SEM model by satisfying all the model fit indices.The linkage variables identified are Green manufacturing, Green Procurement, Green marketing and Distribution, Green purchasing, Supplier cooperation, Customer cooperation, Environmental strategies and management, Environmental Participation and Green training that are forming the driving force for practicing green supply chain. Research limitations/implications: The results of the study are restricted to manufacturing industries, which might vary when applied for other sectors. The developed model on green supply chain management practices would help policy makers, decision makers, researchers and industry professionals to anticipate potential success factors to implement green supply chain practices. Accordingly, the focus on critical success factors would be prioritized for obtaining better performance of supply chain and greening the chain

Optimal Portfolio Management in Alaska: A Case Study on Risk Characteristics of Environmental Consulting Companies

A Project Submitted in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in Project ManagementSharp declines in global oil prices have led to a marked contraction in Alaska’s natural resource dependent economy. This, coupled with record the State’s budgetary shortfalls and a decrease in incoming federal dollars, has created a climate where environmental consulting companies must accept riskier projects to balance portfolio growth and security. As a result, companies must adopt a risk-based portfolio management approach as both a high level strategy and a core management practice. It is important to specifically identify projects best suited for an organization’s tolerance for risk based off of the supply and demand of the industry in rapidly changing economic conditions. Therefore, the aims of this project report are to help environmental consulting companies identify risk characteristics and manage their portfolio, as well as develop a tool to guide decision-making and selecting projects best suited for a companies’ portfolio strategy. The results of this research may provide Alaska based environmental companies with a clear understanding of the types of projects that offer both development and financial security for an organization. This research paper will present the methodology, results, and an environmental consulting portfolio management tool.Title Page / Table of Contents / List of Exhibits / Abstract / Introduction / Background / Literature Review / Project Methodology / Research Methodology / Presentation and Analysis of Data from Survey / Data Validation From Survey / Conclusion / Recommendation / Project Conclusion / Recommendations for Further Research / References / Appendi

Identification of Critical Source Areas (CSAs) and Evaluation of Best Management Practices (BMPs) in Controlling Eutrophication in the Dez River Basin

Best Management Practices (BMPs) are commonly used to control pollution in the river basins. Prioritization of BMPs helps improve the efficiency and effectiveness of pollution reduction, especially in Critical Source Areas (CSAs) that produce the highest pollution loads. Recently, the Dez River in Khuzestan, Iran, has become highly eutrophic from the overuse of fertilizers and pesticides. In this basin, dry and irrigated farming produce 77.34% and 6.3% of the Total Nitrogen (TN) load, and 83.56% and 4.3% of the Total Phosphorus (TP) load, respectively. In addition, residential, pasture, and forest land uses together account for 16.36% of the TN and 12.14% of the TP load in this area. The Soil and Water Assessment Tool (SWAT) was implemented to model the Dez River basin and evaluate the applicability of several BMPs, including point source elimination, filter strips, livestock grazing, and river channel management, in reducing the entry of pollution loads to the river. Sensitivity analysis and calibration/validation of the model was performed using the SUFI-2 algorithm in the SWAT Calibration Uncertainties Program (SWAT-CUP). The CSAs were identified using individual (sediment, TN, TP) and combined indices, based on the amount of pollution produced. Among the BMPs implemented, the 10 m filter strip was most effective in reducing TN load (42.61%), and TP load (39.57%)

Therapists’ experiences and perceptions of teamwork in neurological rehabilitation: Critical happenings in effective and ineffective teamwork

This article reports the second part of an exploratory study into occupational therapists` and physiotherapists` perceptions and experiences of team-work in neurological rehabilitation: the factors that were thought to influence effective and ineffective team-work, and the meaning behind effective and ineffective team work in neurological rehabilitation. The study was undertaken through semi-structured interviews of 10 therapists from three different neurological rehabilitation teams based in the United Kingdom, and used the critical incident technique. Through analysis of the data, several main themes emerged regarding the perceived critical happenings in effective and ineffective team work. These were: team events and characteristics, team members` characteristics, shared and collaborative working practices, communication, specific organisational structures, environmental, external, and patient and family related factors. Effective and ineffective team-work was perceived to impact on a number of levels: having implications for the team, the patient, individual team members, and the neurological rehabilitation service. The study supported the perceived value of team work within neurological rehabilitation. It also indicated the extensive and variable factors that may influence the team working process as well as the complex and diverse nature of the process

Ecological models at fish community and species level to support effective river restoration

RESUMEN Los peces nativos son indicadores de la salud de los ecosistemas acuáticos, y se han convertido en un elemento de calidad clave para evaluar el estado ecológico de los ríos. La comprensión de los factores que afectan a las especies nativas de peces es importante para la gestión y conservación de los ecosistemas acuáticos. El objetivo general de esta tesis es analizar las relaciones entre variables biológicas y de hábitat (incluyendo la conectividad) a través de una variedad de escalas espaciales en los ríos Mediterráneos, con el desarrollo de herramientas de modelación para apoyar la toma de decisiones en la restauración de ríos. Esta tesis se compone de cuatro artículos. El primero tiene como objetivos modelar la relación entre un conjunto de variables ambientales y la riqueza de especies nativas (NFSR), y evaluar la eficacia de potenciales acciones de restauración para mejorar la NFSR en la cuenca del río Júcar. Para ello se aplicó un enfoque de modelación de red neuronal artificial (ANN), utilizando en la fase de entrenamiento el algoritmo Levenberg-Marquardt. Se aplicó el método de las derivadas parciales para determinar la importancia relativa de las variables ambientales. Según los resultados, el modelo de ANN combina variables que describen la calidad de ribera, la calidad del agua y el hábitat físico, y ayudó a identificar los principales factores que condicionan el patrón de distribución de la NFSR en los ríos Mediterráneos. En la segunda parte del estudio, el modelo fue utilizado para evaluar la eficacia de dos acciones de restauración en el río Júcar: la eliminación de dos azudes abandonados, con el consiguiente incremento de la proporción de corrientes. Estas simulaciones indican que la riqueza aumenta con el incremento de la longitud libre de barreras artificiales y la proporción del mesohabitat de corriente, y demostró la utilidad de las ANN como una poderosa herramienta para apoyar la toma de decisiones en el manejo y restauración ecológica de los ríos Mediterráneos. El segundo artículo tiene como objetivo determinar la importancia relativa de los dos principales factores que controlan la reducción de la riqueza de peces (NFSR), es decir, las interacciones entre las especies acuáticas, variables del hábitat (incluyendo la conectividad fluvial) y biológicas (incluidas las especies invasoras) en los ríos Júcar, Cabriel y Turia. Con este fin, tres modelos de ANN fueron analizados: el primero fue construido solamente con variables biológicas, el segundo se construyó únicamente con variables de hábitat y el tercero con la combinación de estos dos grupos de variables. Los resultados muestran que las variables de hábitat son los ¿drivers¿ más importantes para la distribución de NFSR, y demuestran la importancia ecológica de los modelos desarrollados. Los resultados de este estudio destacan la necesidad de proponer medidas de mitigación relacionadas con la mejora del hábitat (incluyendo la variabilidad de caudales en el río) como medida para conservar y restaurar los ríos Mediterráneos. El tercer artículo busca comparar la fiabilidad y relevancia ecológica de dos modelos predictivos de NFSR, basados en redes neuronales artificiales (ANN) y random forests (RF). La relevancia de las variables seleccionadas por cada modelo se evaluó a partir del conocimiento ecológico y apoyado por otras investigaciones. Los dos modelos fueron desarrollados utilizando validación cruzada k-fold y su desempeño fue evaluado a través de tres índices: el coeficiente de determinación (R2 ), el error cuadrático medio (MSE) y el coeficiente de determinación ajustado (R2 adj). Según los resultados, RF obtuvo el mejor desempeño en entrenamiento. Pero, el procedimiento de validación cruzada reveló que ambas técnicas generaron resultados similares (R2 = 68% para RF y R2 = 66% para ANN). La comparación de diferentes métodos de machine learning es muy útil para el análisis crítico de los resultados obtenidos a través de los modelos. El cuarto artículo tiene como objetivo evaluar la capacidad de las ANN para identificar los factores que afectan a la densidad y la presencia/ausencia de Luciobarbus guiraonis en la demarcación hidrográfica del Júcar. Se utilizó una red neuronal artificial multicapa de tipo feedforward (ANN) para representar relaciones no lineales entre descriptores de L. guiraonis con variables biológicas y de hábitat. El poder predictivo de los modelos se evaluó con base en el índice Kappa (k), la proporción de casos correctamente clasificados (CCI) y el área bajo la curva (AUC) característica operativa del receptor (ROC). La presencia/ausencia de L. guiraonis fue bien predicha por el modelo ANN (CCI = 87%, AUC = 0.85 y k = 0.66). La predicción de la densidad fue moderada (CCI = 62%, AUC = 0.71 y k = 0.43). Las variables más importantes que describen la presencia/ausencia fueron: radiación solar, área de drenaje y la proporción de especies exóticas de peces con un peso relativo del 27.8%, 24.53% y 13.60% respectivamente. En el modelo de densidad, las variables más importantes fueron el coeficiente de variación de los caudales medios anuales con una importancia relativa del 50.5% y la proporción de especies exóticas de peces con el 24.4%. Los modelos proporcionan información importante acerca de la relación de L. guiraonis con variables bióticas y de hábitat, este nuevo conocimiento podría utilizarse para apoyar futuros estudios y para contribuir en la toma de decisiones para la conservación y manejo de especies en los en los ríos Júcar, Cabriel y Turia.Olaya Marín, EJ. (2013). Ecological models at fish community and species level to support effective river restoration [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/28853TESI

Modelling manure NPK flows in organic farming systems to minimise nitrate leaching, ammonia volatilization and nitrous oxide emissions (OF0197)

Manure is an important source of organic matter and nutrients in organic farming systems, principally nitrogen (N), phosphorus (P) and potassium (K). Careful management is required during storage, handling and land-spreading to (a) ensure the most efficient use of the nutrients in the farming system and (b) to limit emissions of nitrate (NO3), ammonia (NH3), nitrous oxide (N2O), methane (CH4) and P to the wider environment. With a likely increase in the organically farmed area, information is needed on best practices for manure management in organic systems to minimise the environmental impacts of these systems. The aim was that software would calculate NPK fluxes associated with each aspect of the livestock system, and provide options to explore the impact of management change at key stages in the manure management process. The end point was to be a working prototype model/decision support system (DSS), which we could be demonstrated to a group of organic farmers and used for discussion of the NPK flows in their systems. Most of the effort in this short-term project was spent on three aspects: 1. Developing databases and the underlying model calculations. 2. Developing the software for the prototype system. 3. Limited validation of the output. The two main challenges in the project were (a) allowing a quick and easy representation of the manure management system, which is often complex and (b) being able to represent complex interactions, simply but robustly. The Manure Model (MANMOD) DSS was developed to allow an iconographic-based model representation of individual farm manure management systems to be readily constructed from a library of system components using a 'drag and drop' operation. This allows the user to construct a diagram of connecting components or ‘nodes’ (e.g. manure source, housing system, storage system) which direct and limit the flow pathway of nutrients through the farming system. Each component or node represents a key stage of the system. Once the system has been constructed, pressing the calculation button calculates the following variates for each component of the system: output (i.e. the amounts of N, P and K that will be transferred from that component of the system to the next); balance (i.e. the amount residing in that component of the system); losses (gaseous and ‘leachate’). Workshops were held at the start and end of the project. The following observations were made as a result of this exercise: - The approach is a relatively quick and simple way of constructing manure management systems. However, it is still quite complex, given the complexity of many management systems. - It may be that it is a better tool for advisers so that they can use it for several clients and become more familiar with the tool, compared with a farmer who might use it as a one-off during planning. - Even at its simplest, some detailed information is required – and in units that the farmer may not be familiar with. For example, washdown volume for the hardstanding, amount of straw (kg/animal/month), etc. However, this is not really a reason for not pursuing this information if it will provide an improvement in management. - One value is the option to scenario test. However, this is reliant on the model being sufficiently refined to be able to fairly represent the changes in response to the system. The aim of the project was to produce a prototype system. We have done this, but because of the complexity of the systems that we are trying to represent, we recognise that much more detailed validation of the model is required before it can be disseminated. There are now several Defra-funded studies that could be used in the next phase of the work. (A more detailed summary is available at the start of the main report

A Path to Implement Precision Child Health Cardiovascular Medicine.

Congenital heart defects (CHDs) affect approximately 1% of live births and are a major source of childhood morbidity and mortality even in countries with advanced healthcare systems. Along with phenotypic heterogeneity, the underlying etiology of CHDs is multifactorial, involving genetic, epigenetic, and/or environmental contributors. Clear dissection of the underlying mechanism is a powerful step to establish individualized therapies. However, the majority of CHDs are yet to be clearly diagnosed for the underlying genetic and environmental factors, and even less with effective therapies. Although the survival rate for CHDs is steadily improving, there is still a significant unmet need for refining diagnostic precision and establishing targeted therapies to optimize life quality and to minimize future complications. In particular, proper identification of disease associated genetic variants in humans has been challenging, and this greatly impedes our ability to delineate gene-environment interactions that contribute to the pathogenesis of CHDs. Implementing a systematic multileveled approach can establish a continuum from phenotypic characterization in the clinic to molecular dissection using combined next-generation sequencing platforms and validation studies in suitable models at the bench. Key elements necessary to advance the field are: first, proper delineation of the phenotypic spectrum of CHDs; second, defining the molecular genotype/phenotype by combining whole-exome sequencing and transcriptome analysis; third, integration of phenotypic, genotypic, and molecular datasets to identify molecular network contributing to CHDs; fourth, generation of relevant disease models and multileveled experimental investigations. In order to achieve all these goals, access to high-quality biological specimens from well-defined patient cohorts is a crucial step. Therefore, establishing a CHD BioCore is an essential infrastructure and a critical step on the path toward precision child health cardiovascular medicine