Application of a checklist for quality assistance in environmental modelling to an energy model

Large, complex energy models present considerable challenges to develop and test. Uncertainty assessments of such models provide only partial guidance on the quality of the results. We have developed a model quality assistance checklist to aid in this purpose. The model checklist provides diagnostic output in the form of a set of pitfalls for the model application. The checklist is applied here to an energy model for the problem of assessing energy use and greenhouse gas emissions. Use of the checklist suggests that results on this issue are contingent on a number of assumptions that are highly value-laden. When these assumptions are held fixed, the model is deemed capable of producing moderately robust results of relevance to climate policy over the longer term. Checklist responses also indicate that a number of details critical to policy choices or outcomes on this issue are not captured in the model, and model results should therefore be supplemented with alternative analyses.     

Incompatible Land Uses and the Topology of Cumulative Risk

The extensive literature on environmental justice has, by now, well defined the essential ingredients of cumulative risk, namely, incompatible land uses and vulnerability. Most problematic is the case when risk is produced by a large aggregation of small sources of air toxics. In this article, we test these notions in an area of Southern California, Southeast Los Angeles (SELA), which has come to be known as Asthmatown. Developing a rapid risk mapping protocol, we scan the neighborhood for small potential sources of air toxics and find, literally, hundreds of small point sources within a 2-mile radius, interspersed with residences. We also map the estimated cancer risks and noncancer hazard indices across the landscape. We find that, indeed, such large aggregations of even small, nondominant sources of air toxics can produce markedly elevated levels of risk. In this study, the risk profiles show additional cancer risks of up to 800 in a million and noncancer hazard indices of up to 200 in SELA due to the agglomeration of small point sources. This is significant (for example, estimates of the average regional point-source-related cancer risk range from 125 to 200 in a million). Most importantly, if we were to talk about the risk contour as if they were geological structures, we would observe not only a handful of distinct peaks, but a general “mountain range” running all throughout the study area, which underscores the ubiquity of risk in SELA. Just as cumulative risk has deeply embedded itself into the fabric of the place, so, too, must intervention seek to embed strategies into the institutions and practices of SELA. This has implications for advocacy, as seen in a recently initiated participatory action research project aimed at building health research capacities into the community in keeping with an ethic of care.     

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.     

A model for assessing habitat fragmentation caused by new infrastructures in extensive territories – Evaluation of the impact of the Spanish strategic infrastructure and transport plan

The aim of the present work is to design a model for evaluating the impact of planned infrastructures on species survival at the territorial scale by calculating a connectivity index. The method developed involves determining the effective distance of displacement between patches of the same habitat, simplifying earlier models so that there is no dependence on specific variables for each species. A case study is presented in which the model was used to assess the impact of the forthcoming roads and railways included in the Spanish Strategic Infrastructure and Transport Plan (PEIT, in its Spanish initials).This study took into account the habitats of peninsular Spain, which occupies an area of some 500,000 km². In this territory, the areas deemed to provide natural habitats are defined by Directive 92/43/EEC. The impact of new infrastructures on connectivity was assessed by comparing two scenarios, with and without the plan, for the major new road and railway networks. The calculation of the connectivity index (CI) requires the use of a raster methodology based on the Arc/Info geographical information system (GIS). The actual calculation was performed using a program written in Arc/Info Macro Language (AML); this program is available in FragtULs (Mancebo Quintana, 2007), a set of tools for calculating indicators of fragmentation caused by transport infrastructure (http://topografia.montes.upm.es/fragtuls.html).The indicator of connectivity proposed allows the estimation of the connectivity between all the patches of a territory, with no artificial (non-ecologically based) boundaries imposed. The model proposed appears to be a useful tool for the analysis of fragmentation caused by plans for large territories.     

Pharmacokinetic model of dioxin and furan levels in adipose tissue from Sawmill work involving chlorophenate fungicides

Sawmill workers in British Columbia (B.C.), Canada, have been exposed to chlorophenate fungicides which are known to be contaminated with polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). Due to concern about the potential of these workers to have significant body burdens of PCDD/Fs, and the absence of measurements in these worker, a single-compartment pharmacokinetic model was developed to estimate the concentration of PCDD/Fs in the fat tissue of the sawmill workers. Data from a large cohort of B.C. sawmill workers and literature-based data on chlorophenate exposures and PCDD/F concentrations in chlorophenates were used in Monte Carlo simulations to predict a PCDD/F body burden distribution. The median concentrations of HxCDF and HpCDF predicted using the model for the B.C. sawmill worker population exceeded the range measured in unexposed populations. PeCDF and OCDF concentrations exceeded the range measured in unexposed populations at the 70th percentile of the model-predicted distribution, and PeCDD at the 90th percentile. The primary limitation of the model was the scarcity of input data about actual dermal and inhalation exposures to chlorophenates.     

Ecological restoration as a strategy for mitigating and adapting to climate change: lessons and challenges from Brazil

Climate change is a global phenomenon that affects biophysical systems and human well-being. The Paris Agreement of the United Nations Framework Convention on Climate Change entered into force in 2016 with the objective of strengthening the global response to climate change by keeping global temperature rise this century well below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5 °C. The agreement requires all Parties to submit their “nationally determined contributions” (NDCs) and to strengthen these efforts in the years ahead. Reducing carbon emissions from deforestation and forest degradation is an important strategy for mitigating climate change, particularly in developing countries with large forests. Extensive tropical forest loss and degradation have increased awareness at the international level of the need to undertake large-scale ecological restoration, highlighting the need to identify cases in which restoration strategies can contribute to mitigation and adaptation. Here we consider Brazil as a case study to evaluate the benefits and challenges of implementing large-scale restoration programs in developing countries. The Brazilian NDC included the target of restoring and reforesting 12 million hectares of forests for multiple uses by 2030. Restoration of native vegetation is one of the foundations of sustainable rural development in Brazil and should consider multiple purposes, from biodiversity and ecosystem services conservation to social and economic development. However, ecological restoration still presents substantial challenges for tropical and mega-diverse countries, including the need to develop plans that are technically and financially feasible, as well as public policies and monitoring instruments that can assess effectiveness. The planning, execution, and monitoring of restoration efforts strongly depend on the context and the diagnosis of the area with respect to reference ecosystems (e.g., forests, savannas, grasslands, wetlands). In addition, poor integration of climate change policies at the national and subnational levels and with other sectorial policies constrains the large-scale implementation of restoration programs. The case of Brazil shows that slowing deforestation is possible; however, this analysis highlights the need for increased national commitment and international support for actions that require large-scale transformations of the forest sector regarding ecosystem restoration efforts. Scaling up the ambitions and actions of the Paris Agreement implies the need for a global framework that recognizes landscape restoration as a cost-effective nature-based solution and that supports countries in addressing their remaining needs, challenges, and barriers.

     

Sequential removal of heavy metals ions and organic pollutants using an algal-bacterial consortium

The residual algal-bacterial biomass from photosynthetically supported, organic pollutant biodegradation processes, in enclosed photobioreactors, was tested for its ability to accumulate Cu(II), Ni(II), Cd(II), and Zn(II). Salicylate was chosen as a model contaminant. The algal-bacterial biomass combined the high adsorption capacity of microalgae with the low cost of the residual biomass, which makes it an attractive biosorbent for environmental applications. Cu(II) was preferentially taken-up from the medium when the metals were present both separately and in combination. There was no observed competition for adsorption sites, which suggested that Cu(II), Ni(II), Cd(II), and Zn(II) bind to different sites and that active Ni(II), Cd(II) and Zn(II) binding groups were present at very low concentrations. Therefore, special focus was given to Cu(II) biosorption. Cu(II) biosorption by the algal-bacterial biomass was characterized by an initial fast cell surface adsorption followed by a slower metabolically driven uptake. pH, Cu(II), and algal-bacterial concentration significantly affected the biosorption capacity for Cu(II). Maximum Cu(II) adsorption capacities of 8.5+/-0.4 mg g-1 were achieved at an initial Cu(II) concentration of 20 mg l-1 and at pH 5 for the tested algal-bacterial biomass. These are consistent with values reported for other microbial sorbents under similar conditions. The desorption of Cu(II) from saturated biomass was feasible by elution with a 0.0125 M HCl solution. Simultaneous Cu(II) and salicylate removal in a continuous stirred tank photobioreactor was not feasible due to the high toxicity of Cu(II) towards the microbial culture. The introduction of an adsorption column, packed with the algal-bacterial biomass, prior to the photobioreactor reduced Cu(II) concentration, thereby allowing the subsequent salicylate biodegradation in the photobioreactor.     

Assessment of state-of-the-art models for predicting the remobilisation of radionuclides following the flooding of heavily contaminated areas: the case of Pripyat River floodplain

The performances of models are assessed to predict the wash-off of radionuclides from contaminated flooded areas. This process should be accounted for in the proper management of the aftermath of a nuclear accident. The contamination of the Pripyat River water following the inundation of a floodplain heavily contaminated by (90)Sr and (137)Cs of Chernobyl origin is used as the basis for modelling. The available experimental evidence demonstrated that remobilisation of radiostrontium is an important process implying a significant secondary radioactive load of water flowing over the contaminated floodplain. On the contrary, there is no empirical evidence of a similar behaviour for radiocaesium. In general, state-of-the-art models properly predicted the remobilisation of strontium, whereas they significantly overestimated radiocaesium concentrations in water. The necessary model improvements for a more accurate prediction of radiocaesium contamination levels include a reassessment of the values of the model parameters controlling the remobilisation process.