Analyzing complex water governance regimes: the Management and Transition Framework

This paper presents the Management and Transition Framework (MTF), an interdisciplinary conceptual and methodological framework supporting the analysis of water systems, management processes and multi-level governance regimes. It serves to improve the scientific understanding of system properties and to give practical guidance for the implementation of transition processes towards more adaptive systems. Doing so requires a systemic perspective to embrace complexity and the wealth of interactions characterizing resource governance regimes. The MTF supports and provides guidance for an interdisciplinary approach in the social sciences and across the social–natural science interface.The framework integrates a range of concepts to develop a more coherent understanding of the complexity of water management regimes. Specific emphasis is given to adaptive capacity and learning processes. However, the MTF is not linked to one specific theory but provides a language that can be tailored to specific research questions and different theoretical approaches. The structured yet flexible approach is a condition for developing shared databases that allow comparative analyses over a wider range of case studies. The MTF does not promote panaceas but a diagnostic approach supporting context sensitive analysis without being case specific and thus not directly transferable.     

From green product definitions and classifications to the Green Option Matrix

Hybrid cars, recycled products, photovoltaic cells, bioplastics: why so different products can be called ‘green’? Which practices companies willing to develop green products should implement? How companies can easily and effectively communicate to stakeholders the environmental features of their green products? This paper tries to answer these questions, by developing a Green Option Matrix (GOM), which characterizes green products and practices along different dimensions. This matrix is then used to analyze the different features of green products as well as related green practices developed by a sample of companies belonging to the Dow Jones Sustainability World Index (DJSWI). Relevant data are collected by means of content analysis of companies’ websites and sustainability reports. Green products and practices developed by each company in the sample are positioned in the matrix and results are presented and discussed for each industrial sector. Then, different sectors’ behaviors are compared. The proposed matrix can be used by companies as a market tool to analyze competitors’ green products and practices and as a communication tool to effectively communicate to stakeholders the specific green features of their products and practices.     

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.     

Calibration and Verification of SWMM for Low Impact Development

The Storm Water Management Model was used to simulate runoff and nutrient export from a low impact development (LID) watershed and a watershed using traditional runoff controls. Predictions were compared to observed values. Uncalibrated simulations underpredicted weekly runoff volume and average peak flow rates from the multiple subcatchment LID watershed by over 80%; the single subcatchment traditional watershed had better predictions. Saturated hydraulic conductivity, Manning's n for swales, and initial soil moisture deficit were sensitive parameters. After calibration, prediction of total weekly runoff volume for the LID and traditional watersheds improved to within 12 and 5% of observed values, respectively. For the validation period, predicted total weekly runoff volumes for the LID and traditional watersheds were within 6 and 2% of observed values, respectively. Water quality simulation was less successful, Nash–Sutcliffe coefficients >0.5 for both calibration and validation periods were only achieved for prediction of total nitrogen export from the LID watershed. Simulation of a 100‐year, 24‐h storm resulted in a runoff coefficient of 0.46 for the LID watershed and 0.59 for the traditional watershed. Results suggest either calibration is needed to improve predictions for LID watersheds or expanded look‐up tables for Green–Ampt infiltration parameter values that account for compaction of urban soil and antecedent conditions are needed.     

Assessment of lead pollution in topsoils of a southern Italy area: Analysis of urban and peri-urban environment

Exposure to lead (Pb) may affect adversely human health. Mapping soil Pb contents is essential to obtain a quantitative estimate of potential risk of Pb contamination. The main aim of this paper was to determine the soil Pb concentrations in the urban and peri-urban area of Cosenza–Rende to map their spatial distribution and assess the probability that soil Pb concentration exceeds a critical threshold that might cause concern for human health. Samples were collected at 149 locations from residual and non-residual topsoil in gardens, parks, flower-beds, and agricultural fields. Fine earth fraction of soil samples was analyzed by X-ray Fluorescence spectrometry. Stochastic images generated by the sequential Gaussian simulation were jointly combined to calculate the probability of exceeding the critical threshold that could be used to delineate the potentially risky areas. Results showed areas in which Pb concentration values were higher to the Italian regulatory values. These polluted areas were quite large and likely, they could create a significant health risk for human beings and vegetation in the near future. The results demonstrated that the proposed approach can be used to study soil contamination to produce geochemical maps, and identify hot-spot areas for soil Pb concentration.