Become a member of SEGH


Join a lively, research focussed network, which values and encourages interdisciplinary work across the spectrum of interactions between humans and the environment. 

SEGH has established a series of international conferences and meetings and promotes task force activities to address research and knowledge gaps in the area.  SEGH works with other societies and interest groups to further a better understanding of human interaction.  SEGH members receive a discount against SEGH conference fees.

SEGH has strong links to training and research projects, with a strong emphasis on encouraging young scientists.  Opportunities are developed to enable young researchers to participate in events where experienced professionals from industry and the public sector and academics meet under informal conditions to discuss research findings and relevant gaps in knowledge.

SEGH supports its own cutting edge, impact factor journal: Environmental Geochemistry and Health. In cooperation with Springer, SEGH members can enjoy online access to the journal.

You are warmly invited to join us as returning members or new applicants to the SEGH community.

Full membership: £45, Retired Membership: £25, Student membership: £25 

(Both Full, Retired and Student membership with EGH on-line journal access - please note this includes access to the back catalogue)

Membership without EGH journal: £25

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Membership runs from January to January.  You will need to renew each year using the Join Us button on the homepage and re-enter your details to ensure we have up-to-date information.

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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


    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


    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


    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.