Remarkable sunlight-driven photocatalytic performance of Ag-doped ZnO nanoparticles prepared by green synthesis for degradation of emerging pollutants in water

Environmental Science and Pollution Research - In this work, Ag-doped ZnO nanoparticles (NPs) were synthesized by a simple green method using a toxic agent-free route for photocatalytic purposes,...     

The importance of space, time, and stochasticity to the demography and management of Alliaria petiolata

As population modeling is increasingly called upon to guide policy and management, it is important that we understand not only the central tendencies of our study systems, but the consequences of their variation in space and time as well. The invasive plant Alliaria petiolata (garlic mustard) is actively managed in the United States and is the focus of a developing biological control program. Two weevils (Coleoptera: Curculionidae: Ceutorhynchus) that reduce fecundity (C. alliariae) and rosette survival plus fecundity (C. scrobicollis) are under consideration for release pending host specificity testing. We used a demographic modeling approach to (1) quantify variability in A. petiolata growth and vital rates and (2) assess the potential for single- or multiple-agent biocontrol to suppress growth of 12 A. petiolata populations in Illinois and Michigan studied over three plant generations. We used perturbation analyses and simulation models with stochastic environments to estimate stochastic growth rates (lambda(S)) and predict the probability of successful management using either a single biocontrol agent or two agent species together. Not all populations exhibited invasive dynamics. Estimates of lambda(S) ranged from 0.78 to 2.21 across sites, while annual, deterministic growth (lambda) varied up to sevenfold within individual sites. Given our knowledge of the biocontrol agents, this analysis suggests that C. scrobicollis alone may control A. petiolata at up to 63% of our study sites where lambda >1, with the combination of both agents predicted to succeed at 88% of sites. Across sites and years, the elasticity rankings were dependent on lambda. Reductions of rosette survival, fecundity, or germination of new seeds are predicted to cause the greatest reduction of lambda in growing populations. In declining populations, transitions affecting seed bank survival have the greatest effect on lambda. This contrasts with past analyses that varied parameters individually in an otherwise constant matrix, which may yield unrealistic predictions by decoupling natural parameter covariances. Overall, comparisons of stochastic and deterministic growth rates illustrate how analyses of individual populations or years could misguide management or fail to characterize complex traits such as invasiveness that emerge as attributes of populations rather than species.     

Development and demonstration of a free surface flow-dependent sampling technology

Statistical assessment of water quality data, obtained at the South Florida Water Management District (West Palm Beach, Florida) S-65E structure, illustrated significant differences and inconsistency in nutrient concentrations (Daas and Ebadian, 2003). The evaluation showed that currently used water sampling techniques are inadequate because of deficiencies in functionality and sensitivity to flow conditions at the structure. Moreover, the study indicated the need for a more precise sampling technology, which would consider variations in the velocity profile across the water column during the sampling process. Therefore, a new prototype sampling technology was designed and tested in a pilot open channel. The new sampler had the capability to collect discrete aliquot volumes of water samples that are proportional to the corresponding point velocity at each of these depths. The discrete aliquot volumes were blended to provide an integrated sample that captures the variations in the velocity profile.     

Particle concentrations in data centers

Cooling buildings with large airflow rates of outside air when temperatures are favorable is an established energy-saving measure. In data centers, this strategy is not widely used, owing to concerns that it would cause increased indoor levels of particles of outdoor origin, which could damage electronic equipment. However, environmental conditions typical of data centers and the associated potential for equipment failure are not well characterized. This study presents the first published measurements of particle concentrations in operating data centers. Indoor and outdoor particle measurements were taken at eight different sites in northern California for particulate matter 0.3–5.0 μm in diameter. One of the data centers has an energy-efficient design that employs outside air for cooling, while the rest use conventional cooling methods. Ratios of measured particle concentrations in the conventional data centers to the corresponding outside concentrations were significantly lower than those typically found in office or residential buildings. Estimates using a material-balance model match well with empirical results, indicating that the dominant particle sources and losses have been identified. Measurements taken at the more energy-efficient site show nearly an order of magnitude increase in particle concentration when ventilation rates were high. The model indicates that this increase may be even higher when including particles smaller than the monitoring-equipment size limitation. Even with the increases, the measured particle concentrations are still below concentration limits recommended in industry standards.     

Influence of indoor transport and mixing time scales on the performance of sensor systems for characterizing contaminant releases

Optimizing real-time sensor systems to detect and identify relevant characteristics of an indoor contaminant event is a challenging task. The interpretation of incoming sensor data is confounded by uncertainties in building operation, in the forces driving contaminant transport, and in the physical parameters governing transport. In addition, simulation tools used by the sensor interpretation algorithm introduce modeling uncertainties. This paper explores how the time scales inherent in contaminant transport influence the information that can be extracted from real-time sensor data. In particular, we identify three time scales (within room mixing, room-to-room transport, and removal from the building) and study how they affect the ability of a Bayesian Monte Carlo (BMC) sensor interpretation algorithm to identify the release location and release mass from a set of experimental data, recorded in a multi-floor building. The research shows that some limitations in the BMC approach do not depend on details of the models or the algorithmic implementation, but rather on the physics of contaminant transport. These inherent constraints have implications for the design of sensor systems.     

Optimizing the position of insulating materials in flat roofs exposed to sunshine to gain minimum heat into buildings under periodic heat transfer conditions

Building roofs are responsible for the huge heat gain in buildings. In the present work, an analysis of the influence of insulation location inside a flat roof exposed directly to the sun’s radiation was performed to reduce heat gain in buildings. The unsteady thermal response parameters of the building roof such as admittance, transmittance, decrement factor, and time lags have been investigated by solving a one-dimensional diffusion equation under convective periodic boundary conditions. Theoretical results of four types of walls were compared with the experimental results available in literature. The results reveal that the roof with insulation placed at the outer side and at the center plane of the roof is the most energy efficient from the lower decrement factor point of view and the roof with insulation placed at the center plane and the inner side of the roof is the best from the highest time lag point of view among the seven studied configurations. The composite roof with expanded polystyrene insulation located at the outer side and at the center plane of the roof is found to be the best roof from the lowest decrement factor (0.130) point of view, and the composite roof with resin-bonded mineral wool insulation located at the center plane and at the inner side of the roof is found to be energy efficient from the highest time lag point (9.33 h) of view among the seven configurations with five different insulation materials studied. The optimum fabric energy storage thicknesses of reinforced cement concrete, expanded polystyrene, foam glass, rock wool, rice husk, resin-bonded mineral wool, and cement plaster were computed. From the results, it is concluded that rock wool has the least optimum fabric energy storage thickness (0.114 m) among the seven studied building roof materials.     

Resolving complexities in healthcare waste management: a goal programming approach.

The wide variety of activities at healthcare facilities generates different types of waste. There is always a danger of spreading infection due to mishandling of infectious waste or sharps. Hence, a variety of policies and actions have been taken to improve healthcare waste management systems. A large body of literature is available which suggests methods for tackling different problematic situations but management is confronted with a variety of complex problems, such as the choice of technological options to control infection, legal and budget restrictions and the timely removal of waste, which can, at times, conflict with each other. Hence, a planning model is presented that is based on a trans-shipment goal programming approach wherein the waste flow is optimized for multiple objectives under different priority structures or with different relative importance (weights). The use of the model is demonstrated as a decision-making tool that would help the management to understand the effects of their policies on the system performance. The model is validated for a case application representing a real-life situation. It can be easily seen that, in the case in which the management is biased toward a higher level of safety protection towards infection control, they have to compromise on cost control and to some extent on environmental pollution control.