Genotoxicity in adult residents in mineral coal region—a cross-sectional study

The present study assessed the DNA damage in environmentally exposed volunteers living in seven municipalities in an industrial coal region, through the use of the comet assay with blood cells and the micronucleus test with buccal cells. Blood and buccal smears were collected from 320 male volunteers living in seven cities inserted in a coal region. They were ages of 18 and 50 years and also completed a questionnaire intended to identify factors associated with DNA damage through a Poisson regression analysis. The comet assay detected significant differences in DNA damage in volunteers from different municipalities, and neighboring cities (Pedras Altas, Aceguá, and Hulha Negra) had a higher level of DNA damage in relation to control city. Some of the risk factors associated with identified DNA lesions included residence time and life habits. On the other hand, the micronucleus test did not identify differences between the cities studied, but the regression analysis identified risk factors such as age and life habits (consumption of mate tea and low carbohydrates diet). We conclude that there are differences in the DNA damage of volunteers from different cities of the carboniferous region, but the presence of micronuclei in the oral mucosa does not differ between the same cities. Furthermore, we alert that some related factors may increase the risk of genotoxicity, such as residence location and time, and living and food habits. Finally, we suggest the need for continuous biomonitoring of the population, as well as for investing in health promotion in these vulnerable populations.

     

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.     

Determination of anti-anxiety and anti-epileptic drugs in hospital effluent and a preliminary risk assessment

In this study, an analytical methodology was developed for the determination of psycho-active drugs in the treated effluent of the University Hospital at the Federal University of Santa Maria, RS – Brazil. Samples were collected from point A (Emergency) and point B (General effluent). The adopted methodology included a pre-concentration procedure involving the use of solid phase extraction and determination by liquid chromatography coupled to mass spectrometry. The limit of detection for bromazepam and lorazepam was 4.9 ± 1.0 ng L⁻¹ and, for carbamazepine, clonazepam and diazepam was 6.1 ± 1.5 ng L⁻¹. The limit of quantification was 30.0 ± 1.1 ng L⁻¹, for bromazepam, clonazepam and lorazepam; for carbamazepine was 50.0 ± 1.8 ng L⁻¹ and was 40.0 ± 1.0 ng L⁻¹ for diazepam. The mean concentrations in the Emergency and General effluent treated currents were as follows: for bromazepam, 195 ± 6 ng L⁻¹ and 137 ± 7 ng L⁻¹; for carbamazepine, 590 ± 6 ng L⁻¹ and 461 ± 10 ng L⁻¹; for diazepam, 645 ± 1 ng L⁻¹ and 571 ± 10 ng L⁻¹; for lorazepam, 96 ± 7 ng L⁻¹ and 42 ± 4 ng L⁻¹; and for clonazepam, 134 ± 10 ng L⁻¹ and 57 ± 10 ng L⁻¹. A preliminary risk assessment was conducted: carbamazepine and diazepam require considerable attention owing to their environmental toxicity. The occurrence of these psychoactive-drugs and the environmental risks that they pose demonstrated the need for a more efficient treatment system. As far we are aware, there have been no comparable studies to this on the hazards of hospital effluents in Brazil, and very few that have carried out a risk assessment of psycho-active drugs in hospital effluent in general.     

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.     

High concentrations of polybrominated diphenylethers (PBDEs) in breast adipose tissue of California women

We measured major PBDEs and PCBs in breast adipose tissues of California women participating in a breast cancer study in the late 1990s. Samples were analyzed using gas chromatography with electron impact ionization and tandem mass spectrometry detection. The congener profile observed was: BDE47>BDE99>BDE153>BDE100>BDE154 and PCB153>PCB180>PCB138>PCB118. Whereas high correlations were observed within each chemical class, very weak correlations appeared between classes, pointing to different exposure pathways. Weak negative associations were observed for PBDE congeners and age. Our PBDE data are among the highest reported, exceeding data from the National Health and Nutrition Examination Survey and consistent with the high use of PBDEs in California. These data may be helpful in establishing a baseline for PBDE body burdens to gauge changes over time as a result of restrictions in the use of PBDE formulations.