A Watershed-based Cumulative Risk Impact Analysis: Environmental Vulnerability and Impact Criteria

Swine Concentrated Animal Feeding Operations (CAFOs) have received much attention in recent years. As a result, a watershed-based screening tool, the Cumulative Risk Index Analysis (CRIA), was developed to assess the cumulative impacts of multiple CAFO facilities in a watershedsubunit. The CRIA formula calculates an index number based on: 1) the area of one or more facilities compared to the area of the watershed subunit, 2) the average of the environmental vulnerability criteria, and 3) the average of the industry-specific impact criteria. Each vulnerability or impact criterion is ranked on a 1 to 5 scale, with a low rank indicating low environmental vulnerability or impact and a high rank indicating high environmental vulnerability or impact. The individual criterion ranks, as well as the total CRIA score, can be used to focus the environmental analysis and facilitate discussions with industry, public, and other stakeholders in the Agency decision-making process.     

Prenatal diagnosis using deletion studies in Duchenne muscular dystrophy

Accurate carrier testing and prenatal diagnosis in Duchenne muscular dystrophy (DMD) families is facilitated when an Xp21 deletion is found to be segregating within a family. We discuss the results of the DNA testing in two families, one in which DNA from affected males was available for study and the other in which no DNA from an affected male was available. Factors complicating the counselling of DMD deletion families are outlined.     

The moderating role of calling in the work–family interface: Buffering and substitution effects on employee satisfaction

This research seeks for improved understanding regarding the interaction of meaningful work and the work–family interface. Existing literature suggests that experiencing a sense of calling toward work makes the work domain particularly salient to employees compared to other life domains. In this article, we draw on this idea, rooted in identity theory, to hypothesize that a sense of calling toward work diminishes the effects of work–family conflict and work–family enrichment on employee's job and life satisfaction. We test these ideas in two studies. First, we surveyed an alumni sample of 598 employees from various jobs, industries, and job levels. Then, in a constructive replication, we surveyed 327 employees using a time-lagged design. Calling was found to significantly buffer the effect of work–family conflict on job satisfaction in Study 2, but not Study 1. Calling did not buffer the effect of conflict on life satisfaction in either study. However, both studies demonstrated that calling attenuated (substituted for) the effect of work–family enrichment on job satisfaction. Study 1 supported the idea that calling attenuates the effect of enrichment on life satisfaction; however, this interactive effect was reversed in Study 2, contrary to expectations. We discuss implications for theory and practice related to callings and career choices, as well as for the role of calling and work identity in the work–family interface.     

High-frequency measurements reveal spatial and temporal patterns of dissolved organic matter in an urban water conveyance

Stormwater runoff in urban areas can contribute high concentrations of dissolved organic matter (DOM) to receiving waters, potentially causing impairment to the aquatic ecosystem of urban streams and downstream water bodies. Compositional changes in DOM due to storm events in forested, agricultural, and urban landscapes have been well studied, but in situ sensors have not been widely applied to monitor stormwater contributions in urbanized areas, leaving the spatial and temporal characteristics of DOM within these systems poorly understood. We deployed fluorescent DOM (FDOM) sensors at upstream and downstream locations within a study reach to characterize the spatial and temporal changes in DOM quantity and sources within an urban water conveyance that receives stormwater runoff. Baseflow FDOM decreased over the summer season as seasonal flows upstream transported less DOM. FDOM fluctuated diurnally, the amplitude of which also declined as the summer season progressed. During storms, FDOM concentrations were rapidly elevated to values orders of magnitude greater than baseflow measurements, with greater concentrations at the downstream monitoring site, revealing high contributions from stormwater outfalls between the two locations. Observations from custom, in situ fluorometers resembled results obtained using laboratory methods for identifying DOM source material and indicated that DOM transitioned to a more microbially derived composition as the summer season progressed, while stormwater contributions contributed DOM from terrestrial sources. Deployment of a mobile sensing platform during varying flow conditions captured spatial changes in DOM concentration and composition and revealed contributions of DOM from outfalls during stormflows that would have otherwise been unobserved.     

Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.     

Designing and Implementing a Network for Sensing Water Quality and Hydrology across Mountain to Urban Transitions

Water resources are increasingly impacted by growing human populations, land use, and climate changes, and complex interactions among biophysical processes. In an effort to better understand these factors in semiarid northern Utah, United States, we created a real‐time observatory consisting of sensors deployed at aquatic and terrestrial stations to monitor water quality, water inputs, and outputs along mountain to urban gradients. The Gradients Along Mountain to Urban Transitions (GAMUT) monitoring network spans three watersheds with similar climates and streams fed by mountain winter‐derived precipitation, but that differ in urbanization level, land use, and biophysical characteristics. The aquatic monitoring stations in the GAMUT network include sensors to measure chemical (dissolved oxygen, specific conductance, pH, nitrate, and dissolved organic matter), physical (stage, temperature, and turbidity), and biological components (chlorophyll‐a and phycocyanin). We present the logistics of designing, implementing, and maintaining the network; quality assurance and control of numerous, large datasets; and data acquisition, dissemination, and visualization. Data from GAMUT reveal spatial differences in water quality due to urbanization and built infrastructure; capture rapid temporal changes in water quality due to anthropogenic activity; and identify changes in biological structure, each of which are demonstrated via case study datasets.     

Ecologically Functional Floodplains: Connectivity,Flow Regime,and Scale1

Opperman, Jeffrey J., Ryan Luster, Bruce A. McKenney, Michael Roberts, and Amanda Wrona Meadows, 2010. Ecologically Functional Floodplains: Connectivity, Flow Regime, and Scale. Journal of the American Water Resources Association (JAWRA) 46(2):211-226. DOI: 10.1111/j.1752-1688.2010.00426.x Abstract: This paper proposes a conceptual model that captures key attributes of ecologically functional floodplains, encompassing three basic elements: (1) hydrologic connectivity between the river and the floodplain, (2) a variable hydrograph that reflects seasonal precipitation patterns and retains a range of both high and low flow events, and (3) sufficient spatial scale to encompass dynamic processes and for floodplain benefits to accrue to a meaningful level. Although floodplains support high levels of biodiversity and some of the most productive ecosystems on Earth, they are also among the most converted and threatened ecosystems and therefore have recently become the focus of conservation and restoration programs across the United States and globally. These efforts seek to conserve or restore complex, highly variable ecosystems and often must simultaneously address both land and water management. Thus, such efforts must overcome considerable scientific, technical, and socioeconomic challenges. In addition to proposing a scientific conceptual model, this paper also includes three case studies that illustrate methods for addressing these technical and socioeconomic challenges within projects that seek to promote ecologically functional floodplains through river-floodplain reconnection and/or restoration of key components of hydrological variability.     

A land use regression model for predicting ambient volatile organic compound concentrations in Toronto,Canada

More than 25 studies have employed land use regression (LUR) models to estimate nitrogen oxides and to a lesser extent particulate matter indicators, but these methods have been less commonly applied to ambient concentrations of volatile organic compounds (VOCs). Some VOCs have high plausibility as sources of health effects and others are specific indicators of motor vehicle exhaust. We used LUR models to estimate spatial variability of VOCs in Toronto, Canada. Benzene, n-hexane and total hydrocarbons (THC) were measured from July 25 to August 9, 2006 at 50 locations using the TraceAir organic vapor monitors. Nitrogen dioxide (NO₂) was also sampled to assess its spatial pattern agreement with VOC exposures. Buffers for land use, population density, traffic density, physical geography, and remote sensing measures of greenness and surface brightness were also tested. The remote sensing measures have the highest correlations with VOCs and NO₂ levels (i.e., explains >36% of the variance). Our regression models explain 66–68% of the variance in the spatial distribution of VOCs, compared to 81% for the NO₂ model. The ranks of agreement between various VOCs range from 48 to 63% and increases substantially – up to 75% – for the top and bottom quartile groups. Agreements between NO₂ and VOCs are much smaller with an average rank of 36%. Future epidemiologic studies may therefore benefit from using VOCs as potential toxic agents for traffic-related pollutants.     

Reporting central tendencies of chamber measured surface emission and oxidation

Methane emissions, concentrations, and oxidation were measured on eleven MSW landfills in eleven states spanning from California to Pennsylvania during the three year study. The flux measurements were performed using a static chamber technique. Initial concentration samples were collected immediately after placement of the flux chamber. Oxidation of the emitted methane was evaluated using stable isotope techniques. When reporting overall surface emissions and percent oxidation for a landfill cover, central tendencies are typically used to report “averages” of the collected data. The objective of this study was to determine the best way to determine and report central tendencies. Results showed that 89% of the data sets of collected surface flux have lognormal distributions, 83% of the surface concentration data sets are also lognormal. Sixty seven percent (67%) of the isotope measured percent oxidation data sets are normally distributed. The distribution of data for all eleven landfills provides insight of the central tendencies of emissions, concentrations, and percent oxidation. When reporting the “average” measurement for both flux and concentration data collected at the surface of a landfill, statistical analyses provided insight supporting the use of the geometric mean. But the arithmetic mean can accurately represent the percent oxidation, as measured with the stable isotope technique. We examined correlations between surface CH₄ emissions and surface air CH₄ concentrations. Correlation of the concentration and flux values using the geometric mean proved to be a good fit (R² = 0.86), indicating that surface scans are a good way of identifying locations of high emissions.