Utilizing international networks for accelerating research and learning in transformational sustainability science

A promising approach for addressing sustainability problems is to recognize the unique conditions of a particular place, such as problem features and solution capabilities, and adopt and adapt solutions developed at other places around the world. Therefore, research and teaching in international networks becomes critical, as it allows for accelerating learning by sharing problem understandings, successful solutions, and important contextual considerations. This article identifies eight distinct types of research and teaching collaborations in international networks that can support such accelerated learning. The four research types are, with increasing intensity of collaboration: (1) solution adoption; (2) solution consultation; (3) joint research on different problems; and (4) joint research on similar problems. The four teaching types are, with increasing intensity of collaboration: (1) adopted course; (2) course with visiting faculty; (3) joint course with traveling faculty; and (4) joint course with traveling students. The typology is illustrated by extending existing research and teaching projects on urban sustainability in the International Network of Programs in Sustainability, with partner universities from Europe, North America, Asia, and Africa. The article concludes with challenges and strategies for extending individual projects into collaborations in international networks.     

Assessment of Aquifer Vulnerability in an Agricultural Area in Spain Using the DRASTIC Model

The assessment of aquifer vulnerability is a very important task, especially in agricultural areas because the quality and availability of groundwater affects both the sustainability of agriculture and the quality of life. In this study, an integrated approach is considered, with the use of the generic and agricultural DRASTIC models as well as a geographic information system (GIS), to assess groundwater vulnerability in the agricultural area of Barrax, in the province of Albacete, in Spain. Seven parameters—depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone media, and hydraulic conductivity of the aquifer (DRASTIC)—have been considered as weighted layers to enable an accurate groundwater risk mapping. The results of the generic DRASTIC model indicated very low vulnerability to contamination for Barrax groundwater due to limited urban and industrial development in the wider area. However, agricultural activities impose pressure to groundwater resources and the results of the agricultural DRASTIC model show that 6.86% of the study area is characterized by very high, 2.29% by high, 47.28% by medium, 38.28% by low, and the remaining 5.29% by no vulnerability to groundwater contamination. The distribution of nitrate concentration in groundwater in the area under study is quite well correlated with the agricultural DRASTIC vulnerability index. Sensitivity analysis was also performed to acknowledge statistical uncertainty in the estimation of each parameter used, to assess its impact, and thus to identify the most critical parameters that require further investigation. Depth to water and impact of vadose zone are the parameters that had the most noticeable impact on the generic DRASTIC vulnerability index followed by the soil media and topography. In contrast, the agricultural DRASTIC method is more sensitive to the removal of the depth to water parameter followed by the topography and the soil media parameters.     

Efficiency in heavy metal purge in crustaceans during the ecdysis

Environment, Development and Sustainability - An abundant population of Ucides cordatus swamp crabs is present at Lameirão Ecological Station (Brazil), a tropical ecosystem man-made with...     

Estimating the Genuine Progress Indicator (GPI) for the State of Rio de Janeiro,Brazil, from 2002 to 2016, as a tool for public policy decision making

Environment, Development and Sustainability - This paper estimates the genuine progress indicator (GPI) for the state of Rio de Janeiro (RJ), Brazil, from 2002 to 2016 as an alternative indicator...     

Acute ecotoxicity and genotoxicity assessment of two wastewater treatment units

Environmental Science and Pollution Research - Water contamination by discharge of untreated or poorly treated wastewater into water bodies is a current issue that may cause harm to humans. Water...     

Effects of acidic attack on chemical,mineralogical, and morphological properties of geomaterials

Environmental Science and Pollution Research - Exposure of geomaterials to acidic leachates may compromise their structure and functionality due to changes in physicochemical, mineralogical, and...     

Mapping the Spatial Variability of Plant Diversity in a Tropical Forest: Comparison of Spatial Interpolation Methods

Knowledge of the spatial distribution of plant species is essential to conservation and forest managers in order to identify high priority areas such as vulnerable species and habitats, and designate areas for reserves, refuges and other protected areas. A reliable map of the diversity of plant species over the landscape is an invaluable tool for such purposes. In this study, the number of species, the exponent Shannon and the reciprocal Simpson indices, calculated from 141 quadrat sites sampled in a tropical forest were used to compare the performance of several spatial interpolation techniques used to prepare a map of plant diversity, starting from sample (point) data over the landscape. Means of mapped classes, inverse distance functions, kriging and co-kriging, both, applied over the entire studied landscape and also applied within vegetation classes, were the procedures compared. Significant differences in plant diversity indices between classes demonstrated the usefulness of boundaries between vegetation types, mapped through satellite image classification, in stratifying the variability of plant diversity over the landscape. These mapped classes, improved the accuracy of the interpolation methods when they were used as prior information for stratification of the area. Spatial interpolation by co-kriging performed among the poorest interpolators due to the poor correlation between the plant diversity variables and vegetation indices computed by remote sensing and used as covariables. This indicated that the latter are not suitable covariates of plant diversity indices. Finally, a within-class kriging interpolator yielded the most accurate estimates of plant diversity values. This interpolator not only provided the most accurate estimates by accounting for the indices' intra-class variability, but also provided additional useful interpretations of the structure of spatial variability of diversity values through the interpretation of their semi-variograms. This additional role was found very useful in aiding decisions in conservation planning.