A combination of micro-porous membrane liquid-gas extraction and solid-phase trapping for ultra trace determination of benzene in urine

A method was developed for the determination of benzene in urine. The sample was pumped through the donor channel of a membrane extraction unit with a micro-porous membrane, separating the donor channel from an identical acceptor channel purged with nitrogen. The analyte reached the acceptor channel by diffusion through the membrane and was then swept by the carrier to a solid sorbent tube, where it was trapped. The analyte was subsequently thermally desorbed and analyzed by gas chromatography (GC) with mass selective detection (MS). After optimization, the recovery was close to quantitative, or 95%. Purging the membrane unit with pure water in between the samples eliminated any memory effects. The linearity was good in the concentration range examined (20-4000 ng l(-1)), with a correlation coefficient of 0.9996. The repeatability at 50 ng l(-1) and 400 ng l(-1) was 1.4% and 1.2%, respectively. The limit of detection was 12 ng l(-1) and the limit of quantification 35 ng l(-1). This enables assessment of benzene exposures of occupationally exposed subjects, of smokers and the majority of the general population. The developed method can be easily automated.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Trihalomethanes in Drinking Water of Greater Québec Region (Canada): Occurrence, Variations and Modelling

The levels of trihalomethanes (THMs) – the main species of by-product from water chlorination – were monitored in thedistribution systems of the five major drinking water utilitiesof the greater area of Québec City in order to investigate andmodel their occurrence on a spatial and seasonal basis. Data forTHMs and other water quality and operational parametersassociated with their formation were generated through a 16 monthsampling program involving several sites representing variablewater residence times, from the plant to the system extremity.The results demonstrate that the differences in measured THMlevels between the five utilities are mainly due to the variablequality of raw waters, the type of water treatment process beingused and the type and levels of applied disinfectant. Dependingon the utility, average THM levels were from 1.3 to 2.5 timeshigher in the system extremities than in the water leaving thetreatment plant. Also, average levels of THMs measured in summerat the distribution system extremities were, depending on theutility, from 2.5 to 5 times higher than the average levelsmeasured in winter. The seasonal differences were found to besignificantly greater than those observed by others in waterutilities in the United States and Europe and are explained inlarge part by the considerable changes, over the year, in thequality and temperature of surface waters in Southern Québec. Forthe five utilities under study, multivariate regression modelswere developed in order to predict spatial and seasonalvariations of THMs. Both residual chlorine demand and temperaturewere found to be better, statistically, as predictors for THMoccurrence. The usefulness of the developed models for routineand long term water quality management, as well as for assessmentof human exposure to THMs, are also discussed.     

Sharing the geo-Referenced Results of Climate Change Impact Research

The Prairie Adaptation Research Collaborative (PARC) has implemented an Internet Map Server (IMS) at the PARC web site (www.parc.ca) to 1) disseminate the geo-referenced results of PARC sponsored research on climate change impacts and adaptation, and 2) address data, information and knowledge management within the PARC network of researchers and partners. PARC facilitates interdisciplinary research on adaptation to the impacts of climate change in the Canadian Prairie Provinces. The web site is intended as a platform for sharing information and encouraging discussion of climate change impacts and adaptation. The IMS enables scientists and stakeholders to apply simple climate change scenarios to geo-referenced biophysical and social data, and dynamically create maps that display the geographic distribution of potential impacts of climate change. With a limited capacity for spatial analysis, most geo-processing and the climate impact modeling is done offline within a GIS environment. The IMS will serve the output from climate impact models, such that the model results can be customized by the web site user and be most readily applied to the planning and analysis of adaptation strategies.     

Application of the benthic index of biotic integrity to environmental monitoring in Chesapeake Bay

The Chesapeake Bay benthic index of biotic integrity (B-IBI) was developed to assess benthic community health and environmental quality in Chesapeake Bay. The B-IBI provides Chesapeake Bay monitoring programs with a uniform tool with which to characterize bay-wide benthic community condition and assess the health of the Bay. A probability-based design permits unbiased annual estimates of areal degradation within the Chesapeake Bay and its tributaries with quantifiable precision. However, of greatest interest to managers is the identification of problem areas most in need of restoration. Here we apply the B-IBI to benthic data collected in the Bay since 1994 to assess benthic community degradation by Chesapeake Bay Program segment and water depth. We used a new B-IBI classification system that improves the reliability of the estimates of degradation. Estimates were produced for 67 Chesapeake Bay Program segments. Greatest degradation was found in areas that are known to experience hypoxia or show toxic contamination, such as the mesohaline portion of the Potomac River, the Patapsco River, and the Maryland mainstem. Logistic regression models revealed increased probability of degraded benthos with depth for the lower Potomac River, Patapsco River, Nanticoke River, lower York River, and the Maryland mainstem. Our assessment of degradation by segment and water depth provided greater resolution of relative condition than previously available, and helped define the extent of degradation in Chesapeake Bay.     

Competitive Retention of Lead and Cadmium on an Agricultural Soil

Lead and cadmium contamination of an agricultural soil has been studied using batch and column experiments. Thermodynamics of theretention phenomena may be represented by a Langmuir isotherm foran aqueous metal concentration up to 100 mg L^-1. First order kinetics with respect to the solid phase yield good predictabilityfor both batch and column experiments. Kinetics and thermodynamics of lead retention predominate over those ofcadmium. As a consequence, lead is preferentially retainedand can even displace sorbed cadmium. In the event of anspill involving both metals, cadmium would move further inthe soil and its aqueous concentration downstream could beeven higher than that of the influent solution, increasingpotential risks. A two-region model has been used to fit all the experimental results. Satisfactory predictions for column experiments are obtained with parameters which are consistent with those obtained for the batch experiments, for which sorption is described by a Langmuir isotherm including competitive retention.     

Cumulative effect assessment in Canada: a regional framework for aquatic ecosystems

Sustainable development of the aquatic environment depends upon routine and defensible cumulative effects assessment (CEA). CEA is the process of predicting the consequences of development relative to an assessment of existing environmental quality. Theoretically, it provides an on-going mechanism to evaluate if levels of development exceed the environment's assimilative capacity; i.e., its ability to sustain itself. In practice, the link between CEA and sustainable development has not been realized because CEA concepts and methods have developed along two dichotomous tracks. One track views CEA as an extension of the environmental assessment (EA) process for project developments. Under this track, stressor-based (S-B) methods have been developed where the emphasis is on local, project-related stressors, their link with aquatic indicators, and the potential for environmental effects through stressor-indicator interactions. S-B methods focus on the proposed development and prediction of project-related effects. They lack a mechanism to quantify existing aquatic quality especially at scales broader than an isolated development. This limitation results in the prediction of potential effects relative to a poorly defined baseline state. The other track views CEA as a broader, regional assessment tool where effects-based (E-B) methods specialize in quantification of existing aquatic effects over broad spatial scales. However, the predictive capabilities of E-B methods are limited because they are retrospective, i.e., the stressor causing the effect is identified after the effect has been measured. When used in isolation, S-B and E-B methods do not address CEA in the context necessary for sustainable development. However, if the strengths of these approaches were integrated into a holistic framework for CEA, an operational mechanism would exist to better monitor and assess sustainable development of our aquatic resources. This paper reviews the existing conceptual basis of CEA in Canada including existing methodologies, limitations and strengths. A conceptual framework for integrating project-based and regional-based CEA is presented.     

Investigation of the Influence of Seagrass on Fisheries Catch Level Using Isotonic Regression

The relationship between seagrass area and the commercial catch of some economically important South Australian fish species is not known in any quantitative sense. However, there is evidence in the literature to suggest that seagrass decline will detrimentally impact on species abundance and composition, and consequently that the presence or absence of seagrass will compromise the commercial and recreational fishery. In this paper we describe the construction of models based on order restricted (isotonic) regression and discuss the role of seagrass area, in terms of its influence on the level of commercial catch through modifying the effect of fishing 'effort'.     

Application of a systemic approach to the study of pollution of the Tinto and Odiel rivers (Spain)

The province of Huelva in the SW of Spain presents high environmental contrasts: together with the great abundance of natural spaces, it shows the impacts of historical natural resources exploitation processes. In the Ria of Huelva, the effluents of the chemical industries must be added to the contaminating inputs of the Tinto and Odiel rivers, coming from the acid drainage of the mines located in the Iberian Pyrite Belt. This forced the Environmental Agency (AMA) to elaborate in 1987 an Effluent Remediation Plan in order to negate unacceptable environmental impacts. The application of a grey box systemic analysis to the AMD pollution, undergone by the Tinto and Odiel rivers has allowed to set a conclusive explanation of the sampling results observed for a period of 11 years, thus making available an overall view of the polluting process and, above all, an explanation of its partial aspects.