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.     

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.     

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.     

Linking Land Cover and Water Quality in New York City’S Water Supply Watersheds

The Catskill/Delaware reservoirs supply 90% of New York City’s drinking water. The City has implemented a series of watershed protection measures, including land acquisition, aimed at preserving water quality in the Catskill/Delaware watersheds. The objective of this study was to examine how relationships between landscape and surface water measurements change between years. Thirty-two drainage areas delineated from surface water sample points (total nitrogen, total phosphorus, and fecal coliform bacteria concentrations) were used in step-wise regression analyses to test landscape and surface-water quality relationships. Two measurements of land use, percent agriculture and percent urban development, were positively related to water quality and consistently present in all regression models. Together these two land uses explained 25 to 75% of the regression model variation. However, the contribution of agriculture to water quality condition showed a decreasing trend with time as overall agricultural land cover decreased. Results from this study demonstrate that relationships between land cover and surface water concentrations of total nitrogen, total phosphorus, and fecal coliform bacteria counts over a large area can be evaluated using a relatively simple geographic information system method. Land managers may find this method useful for targeting resources in relation to a particular water quality concern, focusing best management efforts, and maximizing benefits to water quality with minimal costs.The United States Environmental Protection Agency through its Office of Research and Development funded and managed the research described here. It has been subjected to Agency’s administrative review and approved for publication as an EPA document.     

A Spatial Model to Estimate Habitat Fragmentation and Its Consequences on Long-Term Persistence of Animal Populations

The increasing use of the landscape by humans has led to important diminutions of natural surfaces. The remaining patches of wild habitat are small and isolated from each other among a matrix of inhospitable land-uses. This habitat fragmentation, by disabling population movements and stopping their spread to new habitats, is a major threat to the survival of numerous plant and animal species. We developed a general model, adaptable for specific species, capable of identifying suitable habitat patches within fragmented landscapes and investigating the capacity of populations to move between these patches. This approach combines GIS analysis of a landscape, with spatial dynamic modeling. Suitable habitat is identified using a threshold area to perimeter ratio. Potential movement pathways of species between habitat patches are modeled using a cellular automaton. Habitat connectivity is estimated by overlaying habitat patches with movement pathways. The maximum potential population is calculated within and between connected habitat patches and potential risk of inbreeding within meta-populations is considered. The model was tested on a sample map and applied to scenario maps of predicted land-use change in the Peoria Tri-county region (IL). It (1) showed area of natural area alone was insufficient to estimate the consequences on animal populations; (2) underscored the necessity to use approaches investigating the effect of land-use change spatially through the landscape and the importance of considering species-specific life history characteristics; and (3) highlighted the model's potential utility as an indicator of species likelihood to be affected negatively by land-use scenarios and therefore requiring detailed investigation.     

Determination of pollution trends in an abandoned mining site by application of a multivariate statistical analysis to heavy metals fractionation using SM&T-SES

The mobility, availability and persistence of Heavy Metals (HMs), As, Cd, Cu, Ni, Pb and Zn, in contaminated soils of a former abandoned mining area were evaluated by means of a sequential extraction scheme (SES) and applying a multivariate statistical analysis to the obtained data. Chemical partitioning of HMs in each sample was determined in four fractions (acid-soluble, reducible, oxidable and residual) following the Standard Measurements and Testing (SM&T) SES, formerly BCR-SES. Statistical evaluation of results by pattern recognition techniques allowed identification of groups of samples with similar characteristics and observations of correlations between variables, determining the pollution trends and distribution of HMs within the studied area. Typical metal-fraction association and metal availability characteristics of heavy metals have been depicted. The obtained results indicate an urgent need to attenuate the hazard in that area posed by high concentrations of toxic metals, which exceed the limits specified by different European legislations on soil reclamation.     

One-year monitoring survey of organic compounds (PAHs, PCBs, TBT), heavy metals and biomarkers in blue mussels from the Arcachon Bay, France

Marine mussels Mytilus sp. were transplanted on a monthly basis in cages over one year to oyster farms and harbours in the Arcachon Bay (France) in order to assess the water quality of the bay. Contaminant levels (organotin compounds, trace metals, PCBs and PAHs) were measured in tissues of transplanted mussels and mussels from a reference station, along with physiological parameters of the mussels (condition indexes, lipid content and dry weight). Four biomarkers (AChE: acetylcholinesterase activity, GST: gluthathione S-transferase activity, CAT: catalase activity and TBARS: thiobarbituric acid-reactive substance content) were also monitored. The remote stations monitored (oyster parks) exhibited no accumulation pattern of pollutants. Their respective concentrations therefore constitute a background level of the contamination in the bay ([TBT]= 30 ng Sn g(-1) dw, [SigmaHAPs]= 100 ng g(-1) dw, [SigmaPCBs]= 35 ng g(-1) dw). The elevated chemical contamination of the largest harbour of the bay, the Arcachon harbour, can be interpreted in terms of persistence of organotin compounds ([SigmaOTs]= 1500-2000 ng Sn g(-1) dw) and PAHs ([SigmaHAPs]= 4500-5000 ng g(-1) dw) in sediments and, to a lesser extent, of direct inputs of copper ([Cu]= 20 microg g(-1) dw in harbours versus 7 in oyster parks) and petrogenic PAHs ([methylphenanthrenes]= 1600 ng g(-1) dw in the dockyard versus 170 at the gas stations), related to the use of copper-based antifouling paints and to dockyard activity, respectively. However, the Arcachon Bay presents a low contamination level by PCBs and metals, including harbour stations. Furthermore, higher levels of other PAHs (particularly alkyl PAHs such as methylphenanthrenes/1600 ng g(-1) dw) not included in the 16 PAHs from the EPA priority list (usually studied in biomonitoring programmes/1500 ng g(-1) dw) in the Arcachon harbour underline the need to integrate these compounds in biomonitoring of highly PAH-polluted areas such as harbours in order to avoid misinterpretation of the biological responses observed. Biomarker responses were not able to discriminate the different chemical contamination levels recorded in the Arcachon Bay and rather reflected changes in environmental factors. Furthermore, the strong intraspecies variability of biological responses could be due to genetic differences of mussels from the Arcachon Bay. It is the first time that such an integrated monitoring is performed in the Arcachon Bay, also taking into account seasonal variations of chemical contents and biomarkers levels in mussel tissues.