Identification of metal toxicity in sewage sludge leachate

Sewage sludge is a source of organic matter and nutrients with the potential for being used as a fertilizer. However, metals in sewage sludge might accumulate in soil after repeated sludge applications, and metal concentrations might reach concentrations that are toxic to microorganisms, soil organisms and/or plants. This toxicity might change with time due to kinetic factors or abiotic factors such as freezing, drying or rainfall. The objective of this study was to determine toxicity of sewage sludge leachate from a lysimeter with 50 cm of sludge applied. Attempts were also made to identify the cause of toxicity of the sludge leachate by toxicity identification and evaluation (TIE) techniques. Sludge leachate was collected monthly during 1 experimental year (August 2001 to August 2002). Metal concentrations were analysed, and the toxicity was determined with Daphnia magna (48-h immobility). The effect of EDTA or sodium thiosulphate addition, filtration through a CM-resin or a Millex-resin on toxicity was also tested. The results showed that toxicity of the sludge leachate apparently varied during the year, and that filtration through the CM-resin reduced most of the toxicity followed by the addition of EDTA. None of the other treatments reduced the toxicity of the sludge leachate. This indicated that one or more metals were responsible for the observed toxicity. Further calculations of toxic units (TU) suggested that Zn contributed most to the toxicity. Results also indicated that Ca concentrations in the sludge leachate reduced the toxicity of Zn.     

In-vessel composting--bioremediation of aged coal tar soil: effect of temperature and soil/green waste amendment ratio

The biodegradation of 16 United States Environmental Protection Agency (USEPA)-listed polycyclic aromatic hydrocarbons (PAHs) present in contaminated soil from a manufactured gas plant site was investigated using laboratory-scale in-vessel composting-bioremediation reactors over 8 weeks. The influence of temperature (T, 38, 55, and 70 degrees C) and soil/green waste ratio (S:GW, 0.6:1, 0.7:1, 0.8:1, and 0.9:1) was investigated. A comparative study using a temperature profile during in-vessel composting-bioremediation to meet current regulatory requirements was also investigated. Temperature and amendment ratio were found to be important operating parameters for PAH removal for in-vessel composting-bioremediation of aged coal tar-contaminated soil. After 8 weeks of continuous treatment, the highest removal of 16 USEPA PAHs was observed at T=38 degrees C and S:GW=0.8:1 (75.2%). Lower removal of 16 USEPA PAHs was observed for temperature profile treatment (60.8%). We recommend that when conventional composting processes using temperature profiles to meet regulatory requirements for pathogen control need to be used, these should start with a prolonged mesophilic stage (6 weeks in this investigation) followed by thermophilic, cooling, and maturation stages.     

Identifying optimal agricultural countermeasure strategies for a hypothetical contamination scenario using the strategy model

A spatially implemented model designed to assist the identification of optimal countermeasure strategies for radioactively contaminated regions is described. Collective and individual ingestion doses for people within the affected area are estimated together with collective exported ingestion dose. A range of countermeasures are incorporated within the model, and environmental restrictions have been included as appropriate. The model evaluates the effectiveness of a given combination of countermeasures through a cost function which balances the benefit obtained through the reduction in dose with the cost of implementation. The optimal countermeasure strategy is the combination of individual countermeasures (and when and where they are implemented) which gives the lowest value of the cost function. The model outputs should not be considered as definitive solutions, rather as interactive inputs to the decision making process. As a demonstration the model has been applied to a hypothetical scenario in Cumbria (UK). This scenario considered a published nuclear power plant accident scenario with a total deposition of 1.7x10(14), 1.2x10(13), 2.8x10(10) and 5.3x10(9)Bq for Cs-137, Sr-90, Pu-239/240 and Am-241, respectively. The model predicts that if no remediation measures were implemented the resulting collective dose would be approximately 36 000 person-Sv (predominantly from 137Cs) over a 10-year period post-deposition. The optimal countermeasure strategy is predicted to avert approximately 33 000 person-Sv at a cost of approximately 160 million pounds. The optimal strategy comprises a mixture of ploughing, AFCF (ammonium-ferric hexacyano-ferrate) administration, potassium fertiliser application, clean feeding of livestock and food restrictions. The model recommends specific areas within the contaminated area and time periods where these measures should be implemented.     

Derivation of ecotoxicity thresholds for uranium

Assessment of the risk of impact from most radionuclides is based on the total radiological dose rate to the organism of concern. However, for uranium (U) there can be greater risk from chemical toxicity than radiological toxicity (depending on the isotopic composition). Chemical ecotoxicity of U is dependent on several environmental parameters. The most important are carbonate content, because of the formation of soluble carbonate complexes, and divalent cation content (Ca++ and Mg++), because of their competitive interaction with the uranyl ion (UO2++). This study summarizes the literature available to set PNECs (predicted no-effect concentrations) for chemical toxicity of U to non-human biota. The corresponding radiological doses were estimated, and as expected chemical toxicity proved to be the greater concern. There were limited data from some types of biota; however, PNECs for the types of biota of interest were as follows: terrestrial plants--250 mg U kg(-1) dry soil; other soil biota--100 mg U kg(-1) dry soil; freshwater plants--0.005 mg U L(-1) water; freshwater invertebrates--0.005 mg U L(-1) water; freshwater benthos--100 mg U kg(-1) dry sediment; freshwater fish at water hardnesses of: <10 mg CaCO3 L(-1) (very soft water)--0.4 mg U L(-1) water; 10-100 mg CaCO3 L(-1) (soft water)--2.8 mg U L(-1) water; and >100 mg CaCO3 L(-1) (hard water)--23 mg U L(-1) water; or as a function of hardness--0.26 (hardness as mg CaCO3 L(-1); mammals--0.1 mg U kg(-1) body weight d(-1).     

"Reference Biospheres" for solid radioactive waste disposal: the BIOMASS methodology

The BIOMASS Theme 1 project has developed a methodology for the logical and defensible construction of 'assessment biospheres': mathematical representations of biospheres used in the total system performance assessment of radioactive waste disposal. The BIOMASS Methodology provides a systematic approach to decision making, including decisions on how to address biosphere change. The BIOMASS Methodology was developed through consultation and collaboration with many relevant organisations, including regulators, operators and a variety of independent experts. It has been developed to be practical and to be consistent with recommendations from ICRP and IAEA on radiation protection in the context of the disposal of long-lived solid radioactive wastes. The five main steps in the methodology are described in this paper. The importance of a clear assessment context, to clarify intentions and to support a coherent biosphere assessment process within an overall repository performance assessment, is strongly emphasised. A well described assessment context is an important tool for ensuring consistency across the performance assessment as a whole. The use of interaction matrices has been found to be helpful in clarifying the interactions between different habitats within the biosphere system and the significant radionuclide transfer pathways within the biosphere system. Matrices also provide a useful means of checking for consistency.     

Modelling and experimental studies on the transfer of radionuclides to fruit

Although fruit is an important component of the diet, the extent to which it contributes to radiological exposure remains unclear, partially as a consequence of uncertainties in models and data used to assess transfer of radionuclides in the food chain. A Fruits Working Group operated as part of the IAEA BIOMASS (BIOsphere Modelling and ASSessment) programme from 1997 to 2000, with the aim of improving the robustness of the models that are used for radiological assessment. The Group completed a number of modelling and experimental activities including: (i) a review of experimental, field and modelling information on the transfer of radionuclides to fruit; (ii) discussion of recently completed or ongoing experimental studies; (iii) development of a database on the transfer of radionuclides to fruit; (iv) development of a conceptual model for fruit and (v) two model intercomparison studies and a model validation study. The Group achieved significant advances in understanding the processes involved in transfer of radionuclides to fruit. The work demonstrated that further experimental and modelling studies are required to ensure that the current generation of models can be applied to a wide range of scenarios.     

Use of contaminated well water, example reference biospheres 1 and 2A

The BIOMASS programme's Theme 1 evaluated a number of scenarios, which assisted in the development of practical guidance. A total of four Example Reference Biospheres were fully developed, with the assumptions, data, and models thoroughly documented. These Examples display both the practicality and the transparency available through the use of the Reference Biosphere Methodology. While the methodology is designed to promote transparency and traceability, proper documentation and justification is still the responsibility of the user. The Examples can also be used as generic analyses in some situations. Although it is anticipated that each of the Reference Biospheres explored within BIOMASS Theme 1 should be a useful practical example, the quantitative results of the model calculations are not intended to be understood as prescribed biosphere 'conversion factors'. In choosing to implement an Example, careful consideration would need to be given to their relevance (including associated data) to the particular assessment context at hand. In general, the more complex the model is, the more limited applicability it has for generic purposes. For example, ERB1A (direct use of well water for drinking) can be used straightforwardly, with minor or no adjustments, at a number of generic sites. Example 2A, however, for which climatic conditions and agricultural practices need to be specified, would need to be implemented for each specific situation.     

Optimal design for sustainable development of a material recovery facility in a fast-growing urban setting

Installing material recovery facilities (MRFs) in a solid waste management system could be a feasible alternative to achieve sustainable development goals in urban areas if current household and curbside recycling cannot prove successful in the long run. This paper addresses the optimal site selection and capacity planning for a MRF in conjunction with an optimal shipping strategy of solid waste streams in a multi-district urban region. Screening of material recovery and disposal capacity alternatives can be achieved in terms of economic feasibility, technology limitation, recycling potential, and site availability. The optimization objectives include economic impacts characterized by recycling income and cost components for waste management, while the constraint set consists of mass balance, capacity limitation, recycling limitation, scale economy, conditionality, and relevant screening constraints. A case study for the City of San Antonio, Texas (USA) presents a vivid example where scenario planning demonstrates the robustness and flexibility of this modeling analysis. It proves especially useful when determining MRF ownership structure. Each scenario experiences two case settings: (1) two MRF sites are proposed for selection and (2) a single MRF site is sought. Cost analysis confirms processing fees are not the driving force in the City's operation, but rather shipping cost. Sensitivity analysis solidifies the notion that significant public participation plays the most important role in minimizing solid waste management expenses.     

Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling

Both planning and design of municipal solid waste management systems require accurate prediction of solid waste generation. Yet achieving the anticipated prediction accuracy with regard to the generation trends facing many fast-growing regions is quite challenging. The lack of complete historical records of solid waste quantity and quality due to insufficient budget and unavailable management capacity has resulted in a situation that makes the long-term system planning and/or short-term expansion programs intangible. To effectively handle these problems based on limited data samples, a new analytical approach capable of addressing socioeconomic and environmental situations must be developed and applied for fulfilling the prediction analysis of solid waste generation with reasonable accuracy. This study presents a new approach--system dynamics modeling--for the prediction of solid waste generation in a fast-growing urban area based on a set of limited samples. To address the impact on sustainable development city wide, the practical implementation was assessed by a case study in the city of San Antonio, Texas (USA). This area is becoming one of the fastest-growing regions in North America due to the economic impact of the North American Free Trade Agreement (NAFTA). The analysis presents various trends of solid waste generation associated with five different solid waste generation models using a system dynamics simulation tool--Stella. Research findings clearly indicate that such a new forecasting approach may cover a variety of possible causative models and track inevitable uncertainties down when traditional statistical least-squares regression methods are unable to handle such issues.