Engaging Overburdened Communities in Permitting Actions: US Environmental Protection Agency's “Promising Practices” to Promote Environmental Justice

The “promising practices” document represents an important milestone in encouraging meaningful involvement of all populations     

Assessment of contamination from arsenical pesticide use on orchards in the Great Valley region, Virginia and West Virginia, USA

Lead arsenate pesticides were widely used in apple orchards from 1925 to 1955. Soils from historic orchards in four counties in Virginia and West Virginia contained elevated concentrations of As and Pb, consistent with an arsenical pesticide source. Arsenic concentrations in approximately 50% of the orchard site soils and approximately 1% of reference site soils exceed the USEPA Preliminary Remediation Goal (PRG) screening guideline of 22 mg kg(-1) for As in residential soil, defined on the basis of combined chronic exposure risk. Approximately 5% of orchard site soils exceed the USEPA PRG for Pb of 400 mg kg(-1) in residential soil; no reference site soils sampled exceed this value. A variety of statistical methods were used to characterize the occurrence, distribution, and dispersion of arsenical pesticide residues in soils, stream sediments, and ground waters relative to landscape features and likely background conditions. Concentrations of Zn, Pb, and Cu were most strongly associated with high developed land density and population density, whereas elevated concentrations of As were weakly correlated with high orchard density, consistent with a pesticide residue source. Arsenic concentrations in ground water wells in the region are generally <0.005 mg L(-1). There was no spatial association between As concentrations in ground water and proximity to orchards. Arsenic had limited mobility into ground water from surface soils contaminated with arsenical pesticide residues at concentrations typically found in orchards.     

Overexpressing both ATP sulfurylase and selenocysteine methyltransferase enhances selenium phytoremediation traits in Indian mustard

A major goal of our selenium (Se) phytoremediation research is to use genetic engineering to develop fast-growing plants with an increased ability to tolerate, accumulate, and volatilize Se. To this end we incorporated a gene (encoding selenocysteine methyltransferase, SMT) from the Se hyperaccumulator, Astragalus bisulcatus, into Indian mustard (LeDuc, D.L., Tarun, A.S., Montes-Bayón, M., Meija, J., Malit, M.F., Wu, C.P., AbdelSamie, M., Chiang, C.-Y., Tagmount, A., deSouza, M., Neuhierl, B., B?ck, A., Caruso, J., Terry, N., 2004. Overexpression of selenocysteine methyltransferase in Arabidopsis and Indian mustard increases selenium tolerance and accumulation Plant Physiol. 135, 377-383.). The resulting transgenic plants successfully enhanced Se phytoremediation in that the plants tolerated and accumulated Se from selenite significantly better than wild type. However, the advantage conferred by the SMT enzyme was much less when Se was supplied as selenate. In order to enhance the phytoremediation of selenate, we developed double transgenic plants that overexpressed the gene encoding ATP sulfurylase (APS) in addition to SMT, i.e., APSxSMT. The results showed that there was a substantial improvement in Se accumulation from selenate (4 to 9 times increase) in transgenic plants overexpressing both APS and SMT.     

Accuracy of ultrasound diagnoses in pregnancies complicated by suspected fetal anomalies

Referral of pregnancies complicated by suspected fetal anomalies to level III perinatal centres for further evaluation and management is increasing as use of real-time ultrasound spreads, but the sensitivity and specificity of the prenatal diagnoses made in this population are unknown. We undertook a prospective study that followed pregnancies referred to a designated programme dealing with suspected fetal abnormalities. Follow-up of 257 pregnancies revealed that 282 separate anomalies were accurately diagnosed in 212 cases. Normal anatomy was correctly predicted in 42 cases, 16 per cent of the referred population. False-positive and false-negative rates were 1·5 per cent (4/257) and 2 per cent (1/46), respectively. However, 37 per cent of those infants born with anomalies had additional problems not prenatally detected by ultrasound. These results indicate that prenatal ultrasound diagnoses are remarkably accurate overall but that they may be insensitive to associated anomalies in individual cases.     

Natal philopatry and breeding area fidelity of long-billed curlews (Numenius americanus): Patterns and evolutionary consequences

Summary Long-billed curlews (Numenius americanus) appear unique among scolopacid shorebirds so far studied in possessing a significant sex bias in natal philopatry. We resighted 9 curlews at least attempting to breed that were color-banded as chicks; 8 of these were males. Male curlews also cooperate extensively with neighbors in mobbing potential chick predators. This mutualistic behavior may have evolved through kin selection among philopatric males. If so, we would expect such an evolutionary consequence to lead to a similar sex bias in breeding area fidelity. Yet our resightings of colorbanded adults over 4 consecutive years indicate that males and females were equally likely to return to previous nesting territories. Excessive disturbance such as capture and nest loss within a single breeding season was correlated with the likelihood of breeding dispersal by females but not males. This suggests potentially stronger breeding area fidelity of males.     

Particulate organic matter in the sea: the composition conundrum

As organic matter produced in the euphotic zone of the ocean sinks through the mesopelagic zone, its composition changes from one that is easily characterized by standard chromatographic techniques to one that is not. The material not identified at the molecular level is called "uncharacterized". Several processes account for this transformation of organic matter: aggregation/disaggregation of particles resulting in incorporation of older and more degraded material; recombination of organic compounds into geomacromolecules; and selective preservation of specific biomacromolecules. Furthermore, microbial activities may introduce new cell wall or other biomass material that is not easily characterized, or they may produce such material as a metabolic product. In addition, black carbon produced by combustion processes may compose a fraction of the uncharacterized organic matter, as it is not analyzed in standard biochemical techniques. Despite these poorly-defined compositional changes that hinder chemical identification, the vast majority of organic matter in sinking particles remains accessible to and is ultimately remineralized by marine microbes.     

Overview of key operation factors and strategies for improving fermentative volatile fatty acid production and product regulation from sewage sludge

In recent years, volatile fatty acid (VFA) production through anaerobic fermentation of sewage sludge, instead of methane production, has been regarded as a high-value and promising roadmap for sludge stabilization and resource recovery. This review first presents the effects of some essential factors that influence VFA production and composition. In the second part, we present an extensive analysis of conventional pretreatment and co-fermentation strategies ultimately addressed to improving VFA production and composition. Also, the effectiveness of these approaches is summarized in terms of sludge degradation, hydrolysis rate, and VFA production and composition. According to published studies, it is concluded that some pretreatments such as alkaline and thermal pretreatment are the most effective ways to enhance VFA production from sewage sludge. The possible reasons for the improvement of VFA production by different methods are also discussed. Finally, this review also highlights several current technical challenges and opportunities in VFA production with spectrum control, and further related research is proposed.     

Artificial Neural Networks for Risk Decision Support in Natural Hazards: A Case Study of Assessing the Probability of House Survival from Bushfires

Risk decision-making in natural hazards encompasses a plethora of environmental, socio-economic and management-related factors, and benefits greatly from exploring possible patterns and relations among these multivariate factors. Artificial neural networks, capable of general pattern classifications, are potentially well suited for risk decision support in natural hazards. This paper reports an example that assesses the risk patterns or probabilities of house survival from bushfires using artificial neural networks, with a simulation data set based on the empirical study by Wilson and Ferguson (Predicting the probability of house survival during bushfires, Journal of Environmental Management 23 (1986) 259–270). The aim of this study was to re-model and predict the relationship between risk patterns of house survival and a series of independent variables. Various configurations for input and output variables were tested using neural networks. An approach for converting linguistic terms into crisp numbers was used to incorporate linguistic variables into the quantitative neural network analysis. After a series of tests, results show that neural networks are capable of predicting risk patterns under all tested configurations of input and output variables, with a great deal of flexibility. Risk-based mathematical functions, be they linear or non-linear, can be re-modelled using neural networks. Finally, the paper concludes that the artificial neural networks serve as a promising risk decision support tool in natural hazards.