Zeolite formation from coal fly ash and heavy metal ion removal characteristics of thus-obtained Zeolite X in multi-metal systems

Zeolitic materials have been prepared from coal fly ash as well as from a SiO₂–Al₂O₃ system upon NaOH fusion treatment, followed by subsequent hydrothermal processing at various NaOH concentrations and reaction times. During the preparation process, the starting material initially decomposed to an amorphous form, and the nucleation process of the zeolite began. The carbon content of the starting material influenced the formation of the zeolite by providing an active surface for nucleation. Zeolite A (Na-A) was transformed into zeolite X (Na-X) with increasing NaOH concentration and reaction time. The adsorption isotherms of the obtained Na-X based on the characteristics required to remove heavy ions such as Ni²⁺, Cu²⁺, Cd²⁺ and Pb²⁺ were examined in multi-metal systems. Thus obtained experimental data suggests that the Langmuir and Freundlich models are more accurate compared to the Dubinin–Kaganer–Radushkevich (DKR) model. However, the sorption energy obtained from the DKR model was helpful in elucidating the mechanism of the sorption process. Further, in going from a single- to multi-metal system, the degree of fitting for the Freundlich model compared with the Langmuir model was favored due to its basic assumption of a heterogeneity factor. The Extended-Langmuir model may be used in multi-metal systems, but gives a lower value for equilibrium sorption compared with the Langmuir model.     

Household water use behavior: An integrated model

Water authorities are dealing with the challenge of ensuring that there is enough water to meet demand in the face of drought, population growth and predictions of reduced supply due to climate change. In order to develop effective household demand management programs, water managers need to understand the factors that influence household water use. Following an examination and re-analysis of current water consumption behavioral models we propose a new model for understanding household water consumption. We argue that trust plays a role in household water consumption, since people will not save water if they feel others are not minimizing their water use (inter-personal trust). Furthermore, people are less likely to save water if they do not trust the water authority (institutional trust). This paper proposes that to fully understand the factors involved in determining household water use the impact of trust on water consumption needs investigation.     

Long-Term Effects of Changing Land Use Practices on Surface Water Quality in a Coastal River and Lagoonal Estuary

The watershed of the Neuse River, a major tributary of the largest lagoonal estuary on the U.S. mainland, has sustained rapid growth of human and swine populations. This study integrated a decade of available land cover and water quality data to examine relationships between land use changes and surface water quality. Geographic Information Systems (GIS) analysis was used to characterize 26 subbasins throughout the watershed for changes in land use during 1992–2001, considering urban, agricultural (cropland, animal as pasture, and densities of confined animal feed operations [CAFOs]), forested, grassland, and wetland categories and numbers of wastewater treatment plants (WWTPs). GIS was also used together with longitudinal regression analysis to identify specific land use characteristics that influenced surface water quality. Total phosphorus concentrations were significantly higher during summer in subbasins with high densities of WWTPs and CAFOs. Nitrate was significantly higher during winter in subbasins with high numbers of WWTPs, and organic nitrogen was higher in subbasins with higher agricultural coverage, especially with high coverage of pastures fertilized with animal manure. Ammonium concentrations were elevated after high precipitation. Overall, wastewater discharges in the upper, increasingly urbanized Neuse basin and intensive swine agriculture in the lower basin have been the highest contributors of nitrogen and phosphorus to receiving surface waters. Although nonpoint sources have been emphasized in the eutrophication of rivers and estuaries such as the Neuse, point sources continue to be major nutrient contributors in watersheds sustaining increasing human population growth. The described correlation and regression analyses represent a rapid, reliable method to relate land use patterns to water quality, and they can be adapted to watersheds in any region.     

What-You-Look-For-Is-What-You-Find – The consequences of underlying accident models in eight accident investigation manuals

Accident investigation manuals are influential documents on various levels in a safety management system, and it is therefore important to appraise them in the light of what we currently know – or assume – about the nature of accidents. Investigation manuals necessarily embody or represent an accident model, i.e., a set of assumptions about how accidents happen and what the important factors are. In this paper we examine three aspects of accident investigation as described in a number of investigation manuals. Firstly, we focus on accident models and in particular the assumptions about how different factors interact to cause – or prevent – accidents, i.e., the accident “mechanisms”. Secondly, we focus on the scope in the sense of the factors (or factor domains) that are considered in the models – for instance (hu)man, technology, and organization (MTO). Thirdly, we focus on the system of investigation or the activities that together constitute an accident investigation project/process. We found that the manuals all used complex linear models. The factors considered were in general (hu)man, technology, organization, and information. The causes found during an investigation reflect the assumptions of the accident model, following the ‘What-You-Look-For-Is-What-You-Find’ or WYLFIWYF principle. The identified causes typically became specific problems to be fixed during an implementation of solutions. This follows what can be called ‘What-You-Find-Is-What-You-Fix’ or WYFIWYF principle.     

养分(磷)吸收机理模型的应用研究──Ⅱ.模型参数的敏感性分析

采用Claassen的养分吸收机理模型探讨了玉米与大豆幼苗磷吸收过程中的参数敏感性,结果表明：在一般土壤与施肥条件下,玉米与大豆幼苗吸收磷的高度敏感性参数或重要限制因素为根系伸长速率与根半径;在相同土壤供磷条件下,供试大豆幼苗根系的磷摄取能力远低于玉米幼苗根系的磷摄取能力,其原因归结于两者在根系最大养分流Imax与根系伸长速率K两个敏感性参数上的差异;玉米与大豆幼苗磷吸收过程中三种根毛参数的敏感性均较低,这主要与土壤供磷水平较高有关;土壤含水量在玉米幼苗磷吸收过程中表现了极高的敏感性,当土壤含水量降低时,土壤养分供应参数对玉米幼苗磷吸收的敏感性与限制作用均增大。     

No Sustainability without Technical,Fiscal and Social Innovation

Abstract

Migration process has become increasingly important in recent research on population and the environment. A majority of the existing migration and environment literature has focused on the environmental causes and determinants of migration. With the largest rural-to-urban migration flow in world history, and growing concerns about the environmental problems accompanying its fast economic development, China provides a particularly important case for migration and environment research. This paper reviews major migration theories and recent research on environmental effects on migration, with specific attention to the influences of environmental factors on rural-to-urban labor migration in China. A comprehensive multilevel conceptual framework is developed for studying the environmental causes of rural-to-urban migration in China. The effects of land resources on household labor migration decisions in rural China are explored to illustrate the application of this framework.     

Rapid assessment of management options for promoting stock rebuilding in data-poor species under climate change

The development of species recovery plans requires considering likely outcomes of different management interventions, but the complicating effects of climate change are rarely evaluated. We examined how qualitative network models (QNMs) can be deployed to support decision making when data, time, and funding limitations restrict use of more demanding quantitative methods. We used QNMs to evaluate management interventions intended to promote the rebuilding of a collapsed stock of blue king crab (Paralithodes platypus) (BKC) around the Pribilof Islands (eastern Bering Sea) to determine how their potential efficacy may change under climate change. Based on stakeholder input and a literature review, we constructed a QNM that described the life cycle of BKC, key ecological interactions, potential climate-change impacts, relative interaction strengths, and uncertainty in terms of interaction strengths and link presence. We performed sensitivity analyses to identify key sources of prediction uncertainty. Under a scenario of no climate change, predicted increases in BKC were reliable only when stock enhancement was implemented in a BKC hatchery-program scenario. However, when climate change was accounted for, the intervention could not counteract its adverse impacts, which had an overall negative effect on BKC. The remaining management scenarios related to changes in fishing effort on BKC predators. For those scenarios, BKC outcomes were unreliable, but climate change further decreased the probability of observing recovery. Including information on relative interaction strengths increased the likelihood of predicting positive outcomes for BKC approximately 5–50% under the management scenarios. The largest gains in prediction precision will be made by reducing uncertainty associated with ecological interactions between adult BKC and red king crab (Paralithodes camtschaticus). Qualitative network models are useful options when data are limited, but they remain underutilized in conservation.     

Needs-based sewerage prioritization: alternative to conventional cost-benefit analysis

This paper presents an empirical approach to select and prioritize sewerage projects within set budgetary limitations. The methodology includes a model which quantifies benefits of a sewerage project as an index or dimensionless number. The index considers need and urgency of sewerage and other project goals. Benefit is defined as the difference in anticipated impact between the current condition (without the project) and the expected condition with the project. Anticipated benefits primarily include reduction in environmental pollution, reduction of human diseases and morbidity, and other tangible and intangible improvement. This approach is a powerful decision tool for sewerage prioritization and an effective alternative to conventional cost-benefit analysis. Unlike conventional analysis, this approach makes no attempt to convert project benefits and other impacts into a monetary measure. This work recognizes that the decision to provide sewerage based solely on net benefits is not practical. Instead, benefit-cost ratios (B/C) are calculated utilizing cost-effectiveness approach. Using these ratios, 16 unserviced areas of Ensenada, Mexico are ranked. The prioritization rankings produced by this method must be further scrutinized and carefully reviewed for logic, accuracy of input data, and practicality of implementation. A similar framework may also be useful for prioritizing other public works projects.     

Prospective changes in irrigation water requirements caused by agricultural expansion and climate changes in the eastern arc mountains of Kenya

Water resources and land use are closely linked with each other and with regional climate, assembling a very complex system. The understanding of the interconnecting relations involved in this system is an essential step for elaborating public policies that can effectively lead to the sustainable use of water resources. In this study, an integrated modelling framework was assembled in order to investigate potential impacts of agricultural expansion and climate changes on Irrigation Water Requirements (IWR) in the Taita Hills, Kenya. The framework comprised a land use change simulation model, a reference evapotranspiration model and synthetic precipitation datasets generated through a Monte Carlo simulation. In order to generate plausible climate change scenarios, outputs from General Climate Models were used as reference to perturbing the Monte Carlo simulations. The results indicate that throughout the next 20 years the low availability of arable lands in the hills will drive agricultural expansion to areas with higher IWR in the foothills. If current trends persist, agricultural areas will occupy roughly 60% of the study area by 2030. This expansion will increase by approximately 40% the annual water volume necessary for irrigation. Climate change may slightly decrease crops' IWR in April and November by 2030, while in May a small increase will likely be observed. The integrated assessment of these environmental changes allowed a clear identification of priority regions for land use allocation policies and water resources management.     

An evaluation of environmental factors affecting species distributions

Many different models can be built to explain the distributions of species. Often there is no single model that is clearly better than the alternatives, and this leads to uncertainty over which environmental factors are limiting species’ distributions. We investigated the support for different environmental factors by determining the drop in model performance when selected predictors were excluded from the model building process. We used a paired t-test over 37 plant species so that an environmental factor was only deemed significant if it consistently improved the results for multiple species. Geology and winter minimum temperatures were found to be the environmental factors with the most support, with a significant drop in model performance when either of these factors was excluded. However, there was less support for summer maximum temperature, as other environmental factors could combine to produce similar model performance. Our method of evaluating environmental factors using multiple species will not be capable of detecting predictors that are only important for one or two species, but it is difficult to distinguish these from spurious correlations. The strength of the method is that it increases inference for factors that consistently affect the distributions of many species. We discourage the assessment of models against predefined benchmarks, such as an area under the curve (AUC) of more than 0.7, as many alternative models for the same species produce similar results. Therefore, the benchmarks do not provide any indication of how the performance of the selected model compares to alternative models, and they provide weak inference to accept any selected model.