SEGH Events

7th International Workshop on Chemical Bioavailability

04 November 2013
British Geological Survey, Nottingham, UK
The 7th IWCB is a premier event for highlighting research in chemical bioavailability in the environment.

On behalf of the International Organising Committee, the British Geological Survey (BGS) and the University of Nottingham invite everyone to discuss and exchange new and emerging scientific breakthroughs in chemical bioavailability at the 7th International Workshop on Chemical Bioavailability (IWCB). This series is emerging as a premier event for highlighting research in chemical bioavailability in the environment.  We hope that the workshop will provide the opportunity for delegates to exchange knowledge and experience and to further develop a common view on contaminant bioavailability.

Why attend?

  • network with leading figures in the field
  • visit the exhibition to discover new products and services to enhance your research

Call for papers

We invite you to submit an abstract for an oral or poster presentation.  Please use the template on our webpage http://www.bgs.ac.uk/news/events/bioavailabilityWorkshop/home.html and send your completed submission to Cbio7@bgs.ac.uk

 

Themes

  • analytical methodologies
  • models - QSAR for organic bioaccessibility, predictive, spatial, soil properties
  • reference materials
  • case studies on risk based land management
  • microbial bioavailability
  • essential nutrients
  • risk assessment and communication
  • plant uptake
  • chemomimetics
  • sentinel species
  • nano-materials
  • oral, inhalation and dermal pathways

 

Dr Mark Cave, British Geological Survey

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Science in the News

Latest on-line papers from the SEGH journal: Environmental Geochemistry and Health

  • Mechanistic understanding of crystal violet dye sorption by woody biochar: implications for wastewater treatment 2017-08-17

    Abstract

    Dye-based industries, particularly small and medium scale, discharge their effluents into waterways without treatment due to cost considerations. We investigated the use of biochars produced from the woody tree Gliricidia sepium at 300 °C (GBC300) and 500 °C (GBC500) in the laboratory and at 700 °C from a dendro bioenergy industry (GBC700), to evaluate their potential for sorption of crystal violet (CV) dye. Experiments were conducted to assess the effect of pH reaction time and CV loading on the adsorption process. The equilibrium adsorption capacity was higher with GBC700 (7.9 mg g−1) than GBC500 (4.9 mg g−1) and GBC300 (4.4 mg g−1), at pH 8. The CV sorption process was dependent on the pH, surface area and pore volume of biochar (GBC). Both Freundlich and Hill isotherm models fitted best to the equilibrium isotherm data suggesting cooperative interactions via physisorption and chemisorption mechanisms for CV sorption. The highest Hill sorption capacity of 125.5 mg g−1 was given by GBC700 at pH 8. Kinetic data followed the pseudo-second-order model, suggesting that the sorption process is more inclined toward the chemisorption mechanism. Pore diffusion, ππ electron donor–acceptor interaction and H-bonding were postulated to be involved in physisorption, whereas electrostatic interactions of protonated amine group of CV and negatively charged GBC surface led to a chemisorption type of adsorption. Overall, GBC produced as a by-product of the dendro industry could be a promising remedy for CV removal from an aqueous environment.

  • Concentrations, input prediction and probabilistic biological risk assessment of polycyclic aromatic hydrocarbons (PAHs) along Gujarat coastline 2017-08-11

    Abstract

    A comprehensive investigation was conducted in order to assess the levels of PAHs, their input prediction and potential risks to bacterial abundance and human health along Gujarat coastline. A total of 40 sediment samples were collected at quarterly intervals within a year from two contaminated sites—Alang-Sosiya Shipbreaking Yard (ASSBRY) and Navlakhi Port (NAV), situated at Gulf of Khambhat and Gulf of Kutch, respectively. The concentration of ΣPAHs ranged from 408.00 to 54240.45 ng g−1 dw, indicating heavy pollution of PAHs at both the contaminated sites. Furthermore, isomeric ratios and principal component analysis have revealed that inputs of PAHs at both contaminated sites were mixed-pyrogenic and petrogenic. Pearson co-relation test and regression analysis have disclosed Nap, Acel and Phe as major predictors for bacterial abundance at both contaminated sites. Significantly, cancer risk assessment of the PAHs has been exercised based on incremental lifetime cancer risks. Overall, index of cancer risk of PAHs for ASSBRY and NAV ranged from 4.11 × 10−6–2.11 × 10−5 and 9.08 × 10−6–4.50 × 10−3 indicating higher cancer risk at NAV compared to ASSBRY. The present findings provide baseline information that may help in developing advanced bioremediation and bioleaching strategies to minimize biological risk.

  • Error propagation in spatial modeling of public health data: a simulation approach using pediatric blood lead level data for Syracuse, New York 2017-08-08

    Abstract

    Lead poisoning produces serious health problems, which are worse when a victim is younger. The US government and society have tried to prevent lead poisoning, especially since the 1970s; however, lead exposure remains prevalent. Lead poisoning analyses frequently use georeferenced blood lead level data. Like other types of data, these spatial data may contain uncertainties, such as location and attribute measurement errors, which can propagate to analysis results. For this paper, simulation experiments are employed to investigate how selected uncertainties impact regression analyses of blood lead level data in Syracuse, New York. In these simulations, location error and attribute measurement error, as well as a combination of these two errors, are embedded into the original data, and then these data are aggregated into census block group and census tract polygons. These aggregated data are analyzed with regression techniques, and comparisons are reported between the regression coefficients and their standard errors for the error added simulation results and the original results. To account for spatial autocorrelation, the eigenvector spatial filtering method and spatial autoregressive specifications are utilized with linear and generalized linear models. Our findings confirm that location error has more of an impact on the differences than does attribute measurement error, and show that the combined error leads to the greatest deviations. Location error simulation results show that smaller administrative units experience more of a location error impact, and, interestingly, coefficients and standard errors deviate more from their true values for a variable with a low level of spatial autocorrelation. These results imply that uncertainty, especially location error, has a considerable impact on the reliability of spatial analysis results for public health data, and that the level of spatial autocorrelation in a variable also has an impact on modeling results.