A simple,cost-effective multispecies toxicity test using organisms with a cosmopolitan distribution

Difficulties in making accurate, ecosystem-level predictions of environmental effects of chemicals, mixtures, and effluents based solely on the results of tests on single species have necessitated the development of more environmentally realistic, predictive testing methods. This paper describes a multispecies, community-level toxicity test based on the colonization of artificial substrates by microbial species. Tests examined the colonization of initially barren polyurethane foam artificial substrates by Protozoa from a species source colonized in a natural system. Differences in colonization were examined in microecosystems amended with low levels of cadmium, a very toxic heavy metal, and TFM, an organic biocide used against larval sea lamprey. Tests examined differences in colonization over 28 days. For cadmium, effect levels were estimated to be near 1 g 1^–1, in the low range of effect levels determined from chronic single species tests. For TFM, effect levels were estimated to be between 1 and 10 ppm, overlapping the concentrations used in environmental applications. The colonization response, which depends on naked microbial cells reproducing and migrating through toxicant amended water to new substrates, is very sensitive. Tests based on colonization can be adapted to use species from a target receiving system or can use species from a designated natural source. Field validation of these tests can employ nearly identical methods to those used in laboratory studies to assess the accuracy of predictions based on test system data.     

A study on the behaviour of pesticides and their transformation products in the Scheldt estuary using liquid chromatography-electrospray tandem mass spectrometry

Off-line solid-phase extraction (SPE) combined with liquid chromatography-electrospray tandem mass spectrometry (LC-ESI-MS-MS) was used to study the estuarine behaviour of the polar pesticides, atrazine, chloridazon, diuron and metolachlor, and their transformation products (TPs), hydroxyatrazine (HA), desisopropylatrazine (DIA), desethylatrazine (DEA), 3,4-dichlorophenylmethylurea (DPMU) and monuron. The compounds were identified by comparing their LC retention times and product-ion spectra with those of standard solutions. In all but one case the detection limits of the method were sufficient to determine the compounds of interest over the entire salinity range in the estuary. The concentrations of the dissolved pesticides ranged from 70 ng l-1 for chloridazon to 1350 ng l-1 for diuron. The levels of TPs were 3-8% of the levels of their parent pesticide. The mixing plots of polar pesticides and their TPs indicated that TPs, which are present in fresh river water, are conservatively transported to the sea and that no additional amounts of TPs are formed during their transport through the estuary. The one exception was HA, of which approximately 10% of the amount transported to the North Sea is formed in the lower part of the estuary by photochemical oxidation of atrazine. The latter was concluded from the ratios of each analyte over the sum total of the parent pesticide and all TPs along the salinity gradient, which proved to be a useful tool for identifying such estuarine transformations.     

Quantification of protein adducts of hexahydrophthalic anhydride and methylhexahydrophthalic anhydride in human plasma

Hexahydrophthalic anhydride (HHPA) and methylhexahydrophthalic anhydride (MHHPA) are two highly allergenic compounds used in the chemical industry. A method was developed for quantification of protein adducts of HHPA and MHHPA in human plasma. The plasma was dialysed and the anhydrides were hydrolysed from the proteins at mild acidic conditions. The released hexahydrophthalic acid (HHP acid) and methylhexahydrophthalic acid (MHHP acid) were purified by reversed solid phase extraction followed by derivatisation with pentafluorobenzyl bromide. The derivatives were analysed using GC-MS in negative ion chemical ionisation mode with ammonia as moderating gas. As internal standards, deuterium labelled HHP and MHHP acids were used. The detection limits were 0.06 pmol mL(-1) plasma for HHP acid and 0.03 pmol mL(-1) plasma for MHHP acid. The between-day precisions for HHP acid were 18% at 0.3 pmol mL(-1) and 8% at 4 pmol mL(-1). For MHHP acid, the precisions were 13% at 0.3 pmol mL(-1) and 9% at 4 pmol mL(-1). There were strong correlations (r=0.94 for HHPA and 0.99 for MHHPA) between total plasma protein adduct concentrations and serum albumin adduct levels. Workers exposed to time-weighted average air levels of HHPA between < 1 and 340 microg m(-3) and between 2 and 160 microg m(-3) for MHHPA had plasma adduct levels between the detection limits of the methods and 8.40 and 19.0 pmol mL(-1), respectively.     

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.     

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.     

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.     

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.