Soil criteria to protect terrestrial wildlife and open-range livestock from metal toxicity at mining sites

Thousands of hard rock mines exist in the western USA and in other parts of the world as a result of historic and current gold, silver, lead, and mercury mining. Many of these sites in the USA are on public lands. Typical mine waste associated with these sites are tailings and waste rock dumps that may be used by wildlife and open-range livestock. This report provides wildlife screening criteria levels for metals in soil and mine waste to evaluate risk and to determine the need for site-specific risk assessment, remediation, or a change in management practices. The screening levels are calculated from toxicity reference values based on maximum tolerable levels of metals in feed, on soil and plant ingestion rates, and on soil to plant uptake factors for a variety of receptors. The metals chosen for this report are common toxic metals found at mining sites: arsenic, cadmium, copper, lead, mercury, and zinc. The resulting soil screening values are well above those developed by the US Environmental Protection Agency. The difference in values was mainly a result of using toxicity reference values that were more specific to the receptors addressed rather than the most sensitive receptor.     

The New Bedford Harbor Superfund site long-term monitoring program (1993–2009)

New Bedford Harbor (NBH), located in southeastern Massachusetts, was designated as a marine Superfund site in 1983 due to sediment contamination by polychlorinated biphenyls (PCBs). Based on risks to human health and the environment, the first two phases of the site cleanup involved dredging PCB-contaminated sediments from the harbor. Therefore, a long-term monitoring program (LTM) was developed to measure spatial and temporal chemical and biological changes in sediment, water, and biota to assess the effects and effectiveness of the remedial activities. A systematic, probabilistic sampling design was used to select sediment sampling stations. This unbiased design allowed the three segments of the harbor to be compared spatially and temporally to quantify changes resulting from dredging the contaminated sediments. Sediment was collected at each station, and chemical (e.g., PCBs and metals), physical (e.g., grain size), and biological (e.g., benthic community) measurements were conducted on all samples. This paper describes the overall NBH-LTM approach and the results from the five rounds of sample collections. There is a decreasing spatial gradient in sediment PCB concentrations from the northern boundary (upper harbor) to the southern boundary (outer harbor) of the site. Along this same transect, there is an increase in biological condition (e.g., benthic community diversity). Temporally, the contaminant and biological gradients have been maintained since the 1993 baseline collection; however, since the onset of full-scale remediation, PCB concentrations have decreased throughout the site, and one of the benthic community indices has shown significant improvement in the lower and outer harbor areas.     

Large and mesoscale meteo-oceanographic patterns in local responses of biogeochemical concentrations

Investigations surrounding the variability of productivity in upwelling regions are necessary for a better understanding the physical-biological coupling in these regions by monitoring systems of environmental impacts according to the needs of the regional coastal management. Using a spatial and temporal database from National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric (NCAR) Research reanalysis, Quick Scatterometer vector wind, and surface stations from the Southeast coast of Brazil, we investigate the meteorological influences due to the large-scale systems in the variability of the nutrient and larvae concentration, and chlorophyll a, describing statistically relationships between them in upwelling regions. In addition, we used multivariate analysis, such as PCA and clustering to verify spatial and temporal variances and describe more clear the structure and composition of the ecosystem. Correlation matrix analyses were applied for different water masses present in the study area to identify the relations between physical and biogeochemical parameters in a region, where frequently upwelling occur. Statistical approaches and seasonal variability show that the period of November to March is more sensitive to nutrients (1.20?mg/m(3) for chlorophyll a, 2.20?μmol/l for total nitrogen and 5.5?ml/l for DO) and larvae concentrations (120 org/m(3) for most of the larvae, except for cirripedia that presented values around 370 org/m(3)) relating to the influence of large and mesoescale meteorological patterns. The spatial and temporal variables analyzed with multivariate approach show meaningful seasonality variance of the physical and biological samples, characterizing the principal components responsible for this variance in spring and summer (upwelling period), emphasizing the monitoring of species as crustaceans and mussels that are present in the local economy. Then, the spring and summer season are characterized by high productivity due to the occurrence of upwelling in this period.     

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.     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

Development of Emission Factors for Polycarbonate Processing

Abstract

Emission factors for selected volatile organic compounds (VOCs) and particulate emissions were developed while processing eight commercial grades of polycarbonate (PC) and one grade of a PC/acrylonitrile-butadiene-styrene (ABS) blend. A small commercial-type extruder was used, and the extrusion temperature was held constant at 304 °C. An emission factor was calculated for each substance measured and is reported as pounds released to the atmosphere/million pounds of polymer resin processed [ppm (wt/wt)]. Scaled to production volumes, these emission factors can be used by processors to estimate emission quantities from similar PC processing operations.     

Effect of variations in the redox potential of Gleysol on barium mobility and absorption in rice plants

Two assays were designed to obtain information about the influence of redox potential variations on barium mobility and bioavailability in soil. One assay was undertaken in leaching columns, and the other was conducted in pots cultivated with rice (Oryza sativa) using soil samples collected from the surface of Gleysol in both assays. Three doses of barium (100,300 mg kg⁻¹ and 3000 mg kg⁻¹-soil dry weight) and two redox potential values (oxidizing and reducing) were evaluated. During the incubation period, the redox potential (Eh) was monitored in columns and pots until values of −250 mV were reached. After the incubation period, geochemical partitioning was conducted on the barium using the European Communities Bureau of Reference (BCR) method. Rainfall of 200 mm d⁻¹ was simulated in the columns and in the planting of rice seedlings in the pots. The results of the geochemical partitioning demonstrated that the condition of reduction favors increased barium concentrations in the more labile chemical forms and decreased levels in the chemical forms related to oxides. The highest barium concentrations in leached extracts (3.36 mg L⁻¹) were observed at the highest dose and condition of reduction at approximately five times above the drinking water standard. The high concentrations of barium in the soil did not affect plant dry matter production. The highest levels and accumulation of barium in roots, leaves, and grains of rice were found at the highest dose and condition of reduction. These results demonstrate that reduction leads to solubilization of barium sulfate, thereby favoring greater mobility and bioavailability of this element.     

Social learning in a policy-mandated collaboration: community wildfire protection planning in the eastern United States

Policies such as the US Healthy Forests Restoration Act (HFRA) mandate collaboration in planning to create benefits such as social learning and shared understanding among partners. However, some question the ability of top-down policy to foster successful local collaboration. Through in-depth interviews and document analysis, this paper investigates social learning and transformative learning in three case studies of Community Wildfire Protection Planning (CWPP), a policy-mandated collaboration under HFRA. Not all CWPP groups engaged in social learning. Those that did learned most about organisational priorities and values through communicative learning. Few participants gained new skills or knowledge through instrumental learning. CWPP groups had to commit to learning, but the design of the collaborative-mandate influenced the type of learning that was most likely to occur. This research suggests a potential role for top-down policy in setting the structural context for learning at the local level, but also confirms the importance of collaborative context and process in fostering social learning.     

An Analysis of Ozone Generation in Irradiated Houston Air

Ambient ozone measurements in the United States and many other countries are traceable to a National Institute of Standards and Technology Standard Reference Photometer (NIST SRP). The NIST SRP serves as the highest level ozone reference standard in the United States, with NIST SRPs located at NIST and at many U.S. Environmental Protection Agency (EPA) laboratories. The International Bureau of Weights and Measures (BIPM) maintains a NIST SRP as the reference standard for international measurement comparability through the International Committee of Weights and Measures (CIPM). In total, there are currently NIST SRPs located in 20 countries for use as an ozone reference standard. A detailed examination of the NIST SRP by the BIPM and NIST has revealed a temperature gradient and optical path-length bias inherent in all NIST SRPs. A temperature gradient along the absorption cells causes incorrect temperature measurements by as much as 2 °C. Additionally, the temperature probe used for temperature measurements was found to inaccurately measure the temperature of the sample gas due to a self-heating effect. Multiple internal reflections within the absorption cells produce an actual path length longer than the measured fixed length used in the calculations for ozone mole fractions. Reflections from optical filters located at the exit of the absorption cells add to this effect. Because all NIST SRPs are essentially identical, the temperature and path-length biases exist on all units by varying amounts dependent upon instrument settings, laboratory conditions, and absorption cell window alignment. This paper will discuss the cause of, and physical modifications for, reducing these measurement biases in NIST SRPs. Results from actual NIST SRP bias upgrades quantifying the effects of these measurement biases on ozone measurements are summarized.

Implications: NIST SRPs are maintained in laboratories around the world underpinning ozone measurement calibration and traceability within and between countries. The work described in this paper quantifies and shows the reduction of instrument biases in NIST SRPs improving their overall agreement. This improved agreement in all NIST SRPs provides a more stable baseline for ozone measurements worldwide.