Chloramine demand estimation using surrogate chemical and microbiological parameters

A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (F m ) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data. Results showed that the model was able to simulate and estimate chloramine demand at various times in real drinking water systems. To elucidate the loss of chloramine over the wide variation of water quality used in this study, the model incorporates both the fast and slow chloramine decay pathways. The significance of estimated fast and slow decay rate constants as the kinetic parameters of the model for three water sources in Australia was discussed. It was found that with the same water source, the kinetic parameters remain the same. This modelling approach has the potential to be used by water treatment operators as a decision support tool in order to manage chloramine disinfection.     

Economic analysis of adaptive strategies for flood risk management under climate change

Climate change requires reconsideration of flood risk management strategies. Cost-benefit analysis (CBA), an economic decision-support tool, has been widely applied to assess these strategies. This paper aims to describe and discuss probabilistic extensions of CBA to identify welfare-maximising flood risk management strategies under climate change. First, uncertainty about the changes in return periods of hydro-meteorological extremes is introduced by probability-weighted climate scenarios. Second, the analysis is extended by learning about climate change impacts. Learning occurs upon the probabilistic arrival of information. We distinguish between learning from scientific progress, from statistical evidence and from flood disasters. These probabilistic extensions can be used to analyse and compare the economic efficiency and flexibility of flood risk management strategies under climate change. We offer a critical discussion of the scope of such extensions and options for increasing flexibility. We find that uncertainty reduction from scientific progress may reduce initial investments, while other types of learning may increase initial investments. This requires analysing effects of different types of learning. We also find that probabilistic information about climate change impacts and learning is imprecise. We conclude that risk-based CBA with learning improves the flexibility of flood risk management strategies under climate change. However, CBA provides subjective estimates of expected outcomes and reflects different decision-maker preferences than those captured in robustness analyses. We therefore advocate robustness analysis in addition to, or combined with, cost-benefit analysis to support local investment decisions on flood risk reduction and global strategies on allocation of adaptation funds for flood risk management.     

Job requirements for control room jobs in nuclear power plants

Together with other variables, human factors play a central role in the safety of highly complex technical systems such as nuclear power plants. However, despite the unquestionable importance of human factors, little information is available about relevant ability requirements for control room jobs in nuclear power plants. The purpose of this study was to close this gap, to provide specific information about ability requirements for such jobs, and to evaluate how several hypothesized factors (ability domain, type of job, and operating condition) contribute to ability requirements. We found that high levels of cognitive as well as social/interpersonal abilities are needed for control room jobs, and that ability requirements increase with the hierarchical job level for these two domains but decrease for psychomotor/physical abilities and for sensory/perceptual abilities. Furthermore, specifically concerning jobs with a leadership function, we found some differences between incidents and normal operations regarding requirements for social/interpersonal abilities, indicating that the former require a different leadership style than the latter.     

Does your facility have a dust problem: Methods for evaluating dust explosion hazards

The hazards of dust explosions prevailing in plants are dependent on a large variety of factors that include process parameters, such as pressure, temperature and flow characteristics, as well as equipment properties, such as geometry layout, the presence of moving elements, dust explosion characteristics and mitigating measures. A good dust explosion risk assessment is a thorough method involving the identification of all hazards, their probability of occurrence and the severity of potential consequences. The consequences of dust explosions are described as consequences for personnel and equipment, taking into account consequences of both primary and secondary events.While certain standards cover all the basic elements of explosion prevention and protection, systematic risk assessments and area classifications are obligatory in Europe, as required by EU ATEX and Seveso II directives. In the United States, NFPA 654 requires that the design of the fire and explosion safety provisions shall be based on a process hazard analysis of the facility, process, and the associated fire or explosion hazards. In this paper, we will demonstrate how applying such techniques as SCRAM (short-cut risk analysis method) can help identify potentially hazardous conditions and provide valuable assistance in reducing high-risk areas. The likelihood of a dust explosion is based on the ignition probability and the probability of flammable dust clouds arising. While all possible ignition sources are reviewed, the most important ones include open flames, mechanical sparks, hot surfaces, electric equipment, smoldering combustion (self-ignition) and electrostatic sparks and discharges. The probability of dust clouds arising is closely related to both process and dust dispersion properties.Factors determining the consequences of dust explosions include how frequently personnel are present, the equipment strength, implemented consequence-reducing measures and housekeeping, as risk assessment techniques demonstrate the importance of good housekeeping especially due to the enormous consequences of secondary dust explosions (despite their relatively low probability). The ignitibility and explosibility of the potential dust clouds also play a crucial role in determining the overall risk.Classes describe both the likelihood of dust explosions and their consequences, ranging from low probabilities and limited local damage, to high probability of occurrence and catastrophic damage. Acceptance criteria are determined based on the likelihood and consequence of the events. The risk assessment techniques also allow for choosing adequate risk reducing measures: both preventive and protective. Techniques for mitigating identified explosions risks include the following: bursting disks and quenching tubes, explosion suppression systems, explosion isolating systems, inerting techniques and temperature control. Advanced CFD tools (DESC) can be used to not only assess dust explosion hazards, but also provide valuable insight into protective measures, including suppression and venting.     

Use of SOFEA to predict 1,3-D concentrations in air in high-use regions of California

Methyl bromide, a commonly used soil fumigant, is being phased out per the Montreal Protocol and multiple fumigants are being positioned as replacements. Most effective soil fumigants, including methyl bromide, have the potential for inhalation exposure if the material volatilizes from soil. Chronic exposures for the fumigant 1,3-dichloropropene (1,3-D) are managed in part by the California Department of Pesticide Regulation by limiting the annual amount that can be used within a given township. A stochastic/deterministic numerical system (SOil Fumigant Exposure Assessment system [SOFEA]) was developed using the USEPA air dispersion model ISCST3, field study observations for flux loss, and links to Geographic Information Systems (GIS). SOFEA was used retrospectively to simulate concentrations of 1,3-D in air for direct comparison with monitoring program observations conducted by California Air Resources Board in Fresno County. These results indicated slight overprediction but correct magnitudes for regional air concentrations, especially at the higher percentiles, and provide a performance test. SOFEA was also used, prospectively, to predict air concentrations in potential future-use scenarios. These simulations of chronic air concentrations in two high-use 1,3-D counties of California (Ventura, Merced) consisted of 25 contiguous townships treated either at 1.5 times the current township allocation (40,937 kg) or at the maximum levels of 1,3-D used between 1999 and 2006. Exposure predictions for large regions are necessary to evaluate chronic population-based lifetime exposure and risk to 1,3-D should use patterns change. SOFEA provides a tool to estimate regional air concentrations within high-use areas required for such risk assessments.     

Coupling field observations, soil modeling, and air dispersion algorithms to estimate 1,3-dichloropropene and chloropicrin flux and exposure

Soil fumigants are volatile compounds applied to agricultural land to control nematode populations, weeds, and crop diseases. Field trials used for measuring fumigant loss from soil to the atmosphere encompass only a small proportion of the near semi-infinite parameter combinations of environmental, agronomic, and meteorological conditions. One approach to supplement field observations uses a soil physics model for fumigant emission predictions. A model is first validated against existing field study observations and then used to extrapolate results to a wider range of edaphic and climatic conditions. This work compares field observations of 1,3-dichloropropene and chloropicrin emissions to predictions from the USDA soil model CHAIN_2D. Comparison between model predictions and field observations for a Florida and California study had values between 0.62 to 0.81 and 0.99 to 1.0 for discrete and cumulative emission flux, respectively. CHAIN_2D emission rates were then coupled to several USEPA air dispersion models (ISCST3, CALPUFF6) to extend emission estimates to near field air concentrations. CALPUFF6 predicted slightly higher 1-h maximum air concentrations than ISCST3 for the same source strength (26.2-36.0% for setbacks between 1 and 250 m from the field edge, respectively). A sensitivity analysis for the CHAIN_2D/ISCST3 coupled numerical system is provided, with several soil and irrigation parameters consistently the most sensitive. Changes in the depth of incorporation, tarp material, and initial soil water content illustrate the predicted impact to emission strength and resulting near-field air concentrations with reductions of cumulative emission loss from 8.1 to 71% and average 1-h maximum air concentration reductions between 6.2 and 41% depending on the mitigation strategy chosen. Additionally, a stochastic framework based on the published SOFEA system that couples variability in experiment, model sensitivity, and site specific attributes is outlined should regional air concentration estimates resulting from fumigant use be sought.     

Policy, personal dispositions and the evaluation of aircraft noise

In this paper, we hypothesize and test the ideas that (1) people’s subjectivity in relation to aircraft noise is shaped by the policy discourse, (2) this results in a limited number of frames towards aircraft noise, (3) the frames inform people how to think and feel about aircraft noise and (4) the distribution of the frames in the population is dependent on structural variables related to the individual. To reveal subjects’ frames of aircraft noise a latent class model is estimated based on survey data gathered among a sample of 250 residents living near Amsterdam Airport Schiphol, a major international airport in the Netherlands. In line with expectations, the results show that there are four evaluative frames of aircraft noise, three of which are strongly linked to the policy discourse. The frames are associated with fitting levels of annoyance response. In turn, frame membership is influenced by two structural variables, namely aircraft noise exposure and noise sensitivity. The results indicate that social factors operate discursively in the explanation of subjective reaction to noise, while psychological factors operate within a traditional cause-and-effect model. The paper concludes with several policy implications.     

Science-Policy Interactions in MPA Site Selection in the Dutch Part of the North Sea

At the 7th conference of the parties to the Convention on Biological Diversity (CBD-COP7, Kuala Lumpur, 2004) it was agreed to establish a global network of marine and coastal protected areas by 2012. The defined objectives of this MPA-network are based on the ecosystem approach: to protect biodiversity and other ecological values, and to ensure sustainable use. The (inter)national policy guidelines state that the selection of MPAs should be based on scientific information and ecological criteria only. As a signatory to the Convention, the Netherlands is now faced with meeting this obligation, and the process of designating the first Marine Protected Areas (MPAs) in the Dutch part of the North Sea is currently in progress. We focus on the science–policy interactions that are part of this Dutch MPA selection process. By taking a closer look at the contemporary site selection process as well as its historical background, we show that ecological, socio-economic and political considerations cannot always be easily separated. Uncertainty is high and the ultimate selection and delimitation of candidate sites rather seems to be the result of a balancing act between ecological, socio-economic and political interests, in which scientific and policy guiding procedures blend with ad-hoc political decision making, and with expert judgment in cases where data is lacking. As such, this paper presents an example of present-day environmental policy making in action.     

Predicting δCDIC dynamics in CCS: A scheme based on a review of inorganic carbon chemistry under elevated pressures and temperatures

Stable carbon isotopes are important tools to assess potential storage sites for CO₂, as they allow the quantification of ionic trapping via isotope mass balances. In deep geological formations high p/T conditions need to be considered, because CO₂ dissolution, equilibrium constants and isotope fractionation of dissolved inorganic carbon (DIC) depend on temperature, pressure and solute composition. After reviewing different approaches to account for these dependencies, an expanded scheme is presented for speciation and carbon isotope fractionation of DIC and dissolution of CaCO₃ for pCO₂ up to 100 bar, pH down to 3 and temperatures of up to 200 °C. The scheme evaluates the influence of respective parameters on isotope ratios during CO₂ sequestration. The pCO₂ and pH are the dominant controlling factors in the DIC/δ¹³C/pH system. The fugacity of CO₂ has major impact on DIC concentrations at temperatures below 100 °C at high pCO₂. Temperature dependency of activities and equilibrium dominates at temperatures above 100 °C. Isotope ratios of DIC are expected to be about 1–2‰ more depleted in ¹³C compared to the free CO₂ at pCO₂ values above 10 bar. This depletion is controlled by carbon isotope fractionation between CO₂ and H₂CO₃* which is the dominant species of DIC at the resulting pH below 5.     

Recovery of plant species richness during long-term fertilization of a species-rich grassland

Nutrient enrichment of habitats (eutrophication) is considered to be one of the main causes of plant diversity decline worldwide. Several experiments have shown a rapid loss of species in the first years after fertilization started. However, little is known about changes in species richness in the long term. Here, we use a 50-year-old field experiment with a range of fertilization treatments in grasslands that were mown twice each year in the center of The Netherlands. We show that species richness in all plots initially declined but started to recover after approximately 25 years of continued fertilization. This was also true for the heavily fertilized treatment (NPK). In NPK-fertilized plots, the decline was strongest and associated with a strong divergence of plant trait composition from the control, reflecting a shift to a plant community adapted to nutrient-rich conditions. During the subsequent period of increase in species richness, the trait composition remained stable. These results show that plant species richness can, at least partially, recover after an initial diversity decline caused by fertilization.