High frequency monitoring of the coastal marine environment using the MAREL buoy

The MAREL Iroise data buoy provides physico-chemical measurements acquired in surface marine water in continuous and autonomous mode. The water is pumped 1.5 m from below the surface through a sampling pipe and flows through the measuring cell located in the floating structure. Technological innovations implemented inside the measuring cell atop the buoy allow a continuous cleaning of the sensor, while injection of chloride ions into the circuit prevents biological fouling. Specific sensors for temperature, salinity, oxygen and fluorescence investigated in this paper have been evaluated to guarantee measurement precision over a 3 month period. A bi-directional link under Internet TCP-IP protocols is used for data, alarms and remote-control transmissions with the land-based data centre. Herein, we present a 29 month record for 4 parameters measured using a MAREL buoy moored in a coastal environment (Iroise Sea, Brest, France). The accuracy of the data provided by the buoy is assessed by comparison with measurements of sea water weekly sampled at the same site as part of SOMLIT (Service d'Observation du Milieu LIToral), the French network for monitoring of the coastal environment. Some particular events (impact of intensive fresh water discharges, dynamics of a fast phytoplankton bloom) are also presented, demonstrating the worth of monitoring a highly variable environment with a high frequency continuous reliable system.     

Evaluation of forest nutrition based on large-scale foliar surveys: are nutrition profiles the way of the future?

This paper introduces the use of nutrition profiles as a first step in the development of a concept that is suitable for evaluating forest nutrition on the basis of large-scale foliar surveys. Nutrition profiles of a tree or stand were defined as the nutrient status, which accounts for all element concentrations, contents and interactions between two or more elements. Therefore a nutrition profile overcomes the shortcomings associated with the commonly used concepts for evaluating forest nutrition. Nutrition profiles can be calculated by means of a neural network, i.e. a self-organizing map, and an agglomerative clustering algorithm with pruning. As an example, nutrition profiles were calculated to describe the temporal variation in the mineral composition of Scots pine and Norway spruce needles in Finland between 1987 and 2000. The temporal trends in the frequency distribution of the nutrition profiles of Scots pine indicated that, between 1987 and 2000, the N, S, P, K, Ca, Mg and Al decreased, whereas the needle mass (NM) increased or remained unchanged. As there were no temporal trends in the frequency distribution of the nutrition profiles of Norway spruce, the mineral composition of the needles of Norway spruce needles subsequently did not change. Interpretation of the (lack of) temporal trends was outside the scope of this example. However, nutrition profiles prove to be a new and better concept for the evaluation of the mineral composition of large-scale surveys only when a biological interpretation of the nutrition profiles can be provided.     

A pilot project to detect and forecast harmful algal blooms in the northern Gulf of Mexico

More timely access to data and information on the initiation, evolution and effects of harmful algal blooms can reduce adverse impacts on valued natural resources and human health. To achieve this in the northern Gulf of Mexico, a pilot project was initiated to develop a user-driven, end-to-end (measurements to applications) observing system. A key strategy of the project is to coordinate existing state, federal and academic programs at an unprecedented level of collaboration and partnership. Resource managers charged with protection of public health and aquatic resources require immediate notice of algal events and a forecast of when, where and what adverse effects will likely occur. Further, managers require integrated analyses and interpretations, rather than raw data, to make effective decisions. Consequently, a functional observing system must collect and transform diverse measurements into usable forecasts. Data needed to support development of forecasts will include such properties as sea surface temperature, winds, currents and waves; precipitation and freshwater flows with related discharges of sediment and nutrients; salinity, dissolved oxygen, and chlorophyll concentrations (in vivo fluorescence); and remotely-sensed spatial images of sea surface chlorophyll concentrations. These data will be provided via a mixture of discrete and autonomous in situ sensing with near real-time data telemetry, and remote sensing from space (SeaWiFS), aircraft (hyperspectral imagery) or land (high-frequency radar). With calibration across these platforms, the project will ultimately provide a 4-dimensional visualization of harmful algae events in a time frame suitable to resource managers.     

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.     

Concentrations and profiles of polychlorinated biphenyls, -dibenzo-p-dioxins and -dibenzofurans in livers of mink from South Carolina and Louisiana,U.S.A

In South Carolina, U.S.A., mink have been reintroduced from two apparently healthy populations to areas where populations haveexisted in the past but have been extirpated. High mortality wasobserved during transport of mink from the source populations. Inorder to elucidate the potential effects of dioxin-like compoundson the survival and reproduction of mink, concentrations of totalpolychlorinated biphenyls (PCBs), p,p-DDE, dioxin-likePCBs, polychlorinated dibenzo-p-dioxins (PCDDs), anddibenzofurans (PCDFs) were measured in livers of mink collectedfrom the source populations in South Carolina and Louisiana. Concentrations of total 2,3,7,8-tetrachlorodibenzo-p-dioxinequivalents (TEQs) for the South Carolina and Louisiana mink were21 and 14 pg g^-1, wet wt., respectively. PCB and TEQ concentrations were close to the threshold values that can, under laboratory conditions, elicit toxic effects in ranchmink. Therefore, any additional exposures of these populations toTEQs might adversely affect their populations.     

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.     

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.     

Metal Pollution Assessment of Sediment and Water in the River Hindon, India

The metal pollution in water and sediment of the River Hindon in western Uttar Pradesh (India) was assessed for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn. The metal concentrations in water showed wide temporal variation compared with bed sediment because of variability in water discharge and variations in suspended solid loadings. Metal concentrations in bed sediments provided a better evaluation of the degree and the extent of contamination in the aquatic environment, Santagarh and Atali being the most polluted sites of the river. The ratio of heavy metals to conservative elements (Fe, Al, etc.) may reveal the geochemical imbalances due to the elevated metal concentrations normally attributed to anthropogenic sources. Metal/Al ratios for the bed sediments of the river Hindon were used to determine the relative mobility and general trend of relative mobility occurred Fe > Mn > Zn > Cr > Ni > Pb > Cu > Cd.