The effect of fluorescent tracers on droplet spectrum,viscosity, and density of pesticide formulations

The most important factor affecting efficacy and drift of pesticide applications is the droplet spectrum. To measure pesticide drift, researchers utilize fluorescent tracers to rapidly quantify spray deposition. Although fluorescent tracers have been used for more than 50 years, no experiments have been performed on the effect they have on the properties of pesticide formulations (density and viscosity) or droplet spectrum, which affect the drift of pesticides. Therefore, we examined the effect of an oil- and water-based tracer on the volume median diameter (VMD), viscosity, and density of oil- and water-based pesticide formulations. In addition, we experimentally fit and demonstrate the utility of using distributions to characterize pesticide droplet spectra. The addition of tracers to both water- and oil-based formulations did not significantly alter the VMD, viscosity, and density. Lognormal distributions provided the best fit for the water- and oil-based formulations with and without tracer. Our results demonstrated that the addition of oil- and water-based tracers do not significantly alter pesticide formulations properties and droplet spectrum, and most likely do not alter the movement of pesticide droplets in the environment.     

Eigenbehaviors: identifying structure in routine

Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James). An erratum to this article can be found at     

A Multispecies Framework for Landscape Conservation Planning

Abstract: Rapidly changing landscapes have spurred the need for quantitative methods for conservation assessment and planning that encompass large spatial extents. We devised and tested a multispecies framework for conservation planning to complement single‐species assessments and ecosystem‐level approaches. Our framework consisted of 4 elements: sampling to effectively estimate population parameters, measuring how human activity affects landscapes at multiple scales, analyzing the relation between landscape characteristics and individual species occurrences, and evaluating and comparing the responses of multiple species to landscape modification. We applied the approach to a community of terrestrial birds across 25,000 km² with a range of intensities of human development. Human modification of land cover, road density, and other elements of the landscape, measured at multiple spatial extents, had large effects on occupancy of the 67 species studied. Forest composition within 1 km of points had a strong effect on occupancy of many species and a range of negative, intermediate, and positive associations. Road density within 1 km of points, percent evergreen forest within 300 m, and distance from patch edge were also strongly associated with occupancy for many species. We used the occupancy results to group species into 11 guilds that shared patterns of association with landscape characteristics. Our multispecies approach to conservation planning allowed us to quantify the trade‐offs of different scenarios of land‐cover change in terms of species occupancy.     

Effect of organic matter and moisture on the calorific value of solid wastes: An update of the Tanner diagram

Objective of the work was to experimentally determine the effect of the organic matter and moisture contents on the calorific value of organic solid wastes. Nine substrates (i.e. newsprint, biodried municipal solid wastes, municipal solid waste derived composts, wastewater sludges, and sea weed derived compost), with organic matter contents that ranged from 12% to 91% (dry weight) were used in the experiments. All substrates were dried and ground and deionized water was artificially added in order to achieve certain target moisture contents per substrate. The higher heating value (HHV) was, then, determined experimentally for each sample using a bomb calorimeter. Best reduced models were developed to describe the higher and lower heating values as a function of organic matter, ash and moisture contents. A triangular plot was constructed and the self-sustained combustion area was determined and compared to that of the Tanner diagram. Response surfaces were drawn to visually assess the effect of organic matter and moisture contents on the calorific value of the wastes.     

Time‐based differences in the effects of positive and negative affectivity on perceived supervisor support and organizational commitment among newcomers

Building on the broaden‐and‐build theory and research on the negativity bias, this study examines how trait affectivity, as a stable predisposition predicting the pattern of emotional responding, shapes newcomers' perceptions of supervisor support and experience of organizational commitment. Using latent growth modeling and data collected at four points in time from newcomers (N = 158), we found the initial level of perceived supervisor support to mediate a negative relationship between negative affectivity and the level of commitment. Moreover, although newcomers experienced a general decrease in perceived supervisor support and a related decrease in commitment, those with high positive affectivity experienced a weaker decrease in perceived supervisor support, which led to a weaker decrease in commitment. Incidentally, positive affectivity was also positively related to the initial level of commitment. Two post hoc studies indicated that positive and negative affectivity exerted their effects controlling for state affect and replicated the relationship between change in perceived support and commitment. We discuss how these findings inform our understanding of trait affectivity's influence on newcomers' work attitudes.     

Simulation Study of a PEM Fuel Cell System with Steam Reforming

Abstract

This article summarizes the results of a study for a 100 kWe DC electrical power PEM fuel cell system. The system consists of a pre-steam reformer, a steam reformer, high and low temperature shift reactors, a preferential oxidation reactor, a PEM fuel cell, a combustor, and an expander. Acceptable net electrical efficiency levels can be achieved via intensive heat integration within the PEM fuel cell system. The calculations take into account the auxiliary equipment such as pumps, com pressors, heaters, coolers, heat exchangers and pipes. The process simulation package “Aspen-HYSYS 3.1’’ has been used. The operation parameters of the reactors have been determined considering all the technical limitations involved. A gasoline type hydrocarbon fuel has been studied as hydrogen rich gas source. Thermal efficiencies have been calculated for all of the major system components for selected operation conditions. The fuel cell stack efficiency has been calculated as a function of cell numbers (500, 750, 1000, and 1250 cells). Efficiencies of all of the major system components along with auxiliary unit efficiencies determine the net electrical efficiency of the PEM fuel cell system. The obtained net electrical efficiency levels are between 34 (500 cells) to 41% (1250 cells).     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

ESTIMATION OF LONG-TERM DISCHARGE STATISTICS BY REGIONAL ADJUSTMENT1

ABSTRACT: A regional adjustment relationship was developed to estimate long-term (30-year) monthly median discharges from short term (three-year) records. This method differs from traditional approaches in that it is based on site-specific discharge data but does not require correlation of these data with discharges from a single hydrologically similar long-term gage. The method is shown to be statistically robust, and applicable to statistics other than the median.     

Nutrient Inputs to the Laurentian Great Lakes by Source and Watershed Estimated Using SPARROW Watershed Models1

Robertson, Dale M. and David A. Saad, 2011. Nutrient Inputs to the Laurentian Great Lakes by Source and Watershed Estimated Using SPARROW Watershed Models. Journal of the American Water Resources Association (JAWRA) 47(5):1011‐1033. DOI: 10.1111/j.1752‐1688.2011.00574.x Abstract: Nutrient input to the Laurentian Great Lakes continues to cause problems with eutrophication. To reduce the extent and severity of these problems, target nutrient loads were established and Total Maximum Daily Loads are being developed for many tributaries. Without detailed loading information it is difficult to determine if the targets are being met and how to prioritize rehabilitation efforts. To help address these issues, SPAtially Referenced Regressions On Watershed attributes (SPARROW) models were developed for estimating loads and sources of phosphorus (P) and nitrogen (N) from the United States (U.S.) portion of the Great Lakes, Upper Mississippi, Ohio, and Red River Basins. Results indicated that recent U.S. loadings to Lakes Michigan and Ontario are similar to those in the 1980s, whereas loadings to Lakes Superior, Huron, and Erie decreased. Highest loads were from tributaries with the largest watersheds, whereas highest yields were from areas with intense agriculture and large point sources of nutrients. Tributaries were ranked based on their relative loads and yields to each lake. Input from agricultural areas was a significant source of nutrients, contributing ～33‐44% of the P and ～33‐58% of the N, except for areas around Superior with little agriculture. Point sources were also significant, contributing ～14‐44% of the P and 13‐34% of the N. Watersheds around Lake Erie contributed nutrients at the highest rate (similar to intensively farmed areas in the Midwest) because they have the largest nutrient inputs and highest delivery ratio.     

Analyzing effective factors on leakage-induced hydrogen fires

Currently, novel energy resources are receiving increasing attention as a response to the limitation in fossil fuels as well as their adverse effects on human health. Hydrogen, one of the most abundant elements on the earth, can be regarded as a new energy source to replace fossil fuels. Therefore, safety assessment of the relating processes is very crucial by increasing use of hydrogen as a fuel source. In this regard, consequence analysis for risk assessment and power reduction is very important. The present study aims at modeling hydrogen dispersion along with consequence analyses for such events as jet fire and flash fire. The model was validated by using the data derived from a study on hydrogen leakage in supply pipelines in the laboratory of the University of Pisa. Modeling results reveal that ambient conditions will impose a milder impact on leakage consequences if internal pressure is high in release source. The safe distance was also estimated to be 14 m. Dispersion consequence modeling was performed, followed by the evaluation of the effect of environmental (i.e., stability, ambient temperature, surface roughness, wind speed, and humidity) and process (i.e., vessel temperature and pressure, leakage diameter, and releasing point height) parameters on maximum size flammable vapor cloud and maximum level jet fire radiation on the ground. The size of flammable vapor cloud (consequence dispersion index) and the maximum flux of radiation were affected by process parameters more than ambient parameters. Leakage diameter and the vessel pressure were found to have the highest impact on the operational parameters.