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.     

Suggested protocol for collecting, handling and preparing peat cores and peat samples for physical, chemical, mineralogical and isotopic analyses

For detailed reconstructions of atmospheric metal deposition using peat cores from bogs, a comprehensive protocol for working with peat cores is proposed. The first step is to locate and determine suitable sampling sites in accordance with the principal goal of the study, the period of time of interest and the precision required. Using the state of the art procedures and field equipment, peat cores are collected in such a way as to provide high quality records for paleoenvironmental study. Pertinent field observations gathered during the fieldwork are recorded in a field report. Cores are kept frozen at -18 degree C until they can be prepared in the laboratory. Frozen peat cores are precisely cut into 1 cm slices using a stainless steel band saw with stainless steel blades. The outside edges of each slice are removed using a titanium knife to avoid any possible contamination which might have occurred during the sampling and handling stage. Each slice is split, with one-half kept frozen for future studies (archived), and the other half further subdivided for physical, chemical, and mineralogical analyses. Physical parameters such as ash and water contents, the bulk density and the degree of decomposition of the peat are determined using established methods. A subsample is dried overnight at 105 degree C in a drying oven and milled in a centrifugal mill with titanium sieve. Prior to any expensive and time consuming chemical procedures and analyses, the resulting powdered samples, after manual homogenisation, are measured for more than twenty-two major and trace elements using non-destructive X-Ray fluorescence (XRF) methods. This approach provides lots of valuable geochemical data which documents the natural geochemical processes which occur in the peat profiles and their possible effect on the trace metal profiles. The development, evaluation and use of peat cores from bogs as archives of high-resolution records of atmospheric deposition of mineral dust and trace elements have led to the development of many analytical procedures which now permit the measurement of a wide range of elements in peat samples such as lead and lead isotope ratios, mercury, arsenic, antimony, silver, molybdenum, thorium, uranium, rare earth elements. Radiometric methods (the carbon bomb pulse of (14)C, (210)Pb and conventional (14)C dating) are combined to allow reliable age-depth models to be reconstructed for each peat profile.     

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.     

A comparison of X-ray fluorescence and wet chemical analysis of air filter samples from a scrap lead smelting operation

Personal and area air samples were taken at a scrap lead smelter operation in a bullet manufacturing facility. Samples were taken using the 37-mm styrene-acrylonitrile closed-face filter cassette (CFC, the current US standard device for lead sampling), the 37-mm GSP or "cone" sampler, the 25-mm Institute of Occupational Medicine (IOM) inhalable sampler, and the 25-mm Button sampler (developed at the University of Cincinnati). Polyvinylchloride filters were used for sampling. The filters were pre- and post-weighed, and analyzed for lead content using a field-portable X-ray fluorescence (XRF) analyzer. The filters were then extracted with dilute nitric acid in an ultrasonic extraction bath and the solutions were analyzed by inductively coupled plasma optical emission spectroscopy. The 25-mm filters were analyzed using a single XRF reading, while three readings on different parts of the filter were taken from the 37-mm filters. The single reading from the 25-mm filters was adjusted for the nominal area of the filter to obtain the mass loading, while the three readings from the 37-mm filters were inserted into two different algorithms for calculating the mass loadings, and the algorithms were compared. The IOM sampler was designed for material collected in the body of the sampler to be part of the collected sample as well as that on the filter. Therefore, the IOM sampler cassettes were rinsed separately to determine if wall-loss corrections were necessary. All four samplers gave very good correlations between the two analytical methods above the limit of detection of the XRF procedure. The limit of detection for the 25-mm filters (5 microg) was lower than for the 37-mm filters (10 microg). The percentage of XRF results that were within 25% of the corresponding ICP results was evaluated. In addition, the bias from linear regression was estimated. Linear regression for the Button sampler and the IOM sampler using single readings and the GSP using all tested techniques for total filter loading gave acceptable XRF readings at loadings equivalent to sampling at the OSHA 8-hour Action Level and Permissible Exposure Limit. However, the CFC only had acceptable results when the center reading corrected for filter area was used, which was surprising, and may be a result of a limited data set. In addition to linear regression, simple estimation of bias indicated reasonable agreements between XRF and ICP results for single XRF readings on the Button sampler filters, (82% of the individual results within criterion), and on the IOM sampler filters (77% or 61%--see text), and on the GSP sampler filters using the OSHA algorithm (78%). As a result of this pilot project, all three samplers were considered suitable for inclusion in further field research studies.     

Development and validation of methods for environmental monitoring of cyclophosphamide in workplaces

Methods to monitor contamination of workplaces with antineoplastic drugs have been developed and validated. Cyclophosphamide (CP) was used as a model compound as it is one of the most commonly used antineoplastic drugs. A wipe sampling method to detect contamination with CP at surfaces was developed. A personal air sampling method to sample gas and vapour on solid sorbent tubes and particles with filters was also developed. Wipe and filter samples were extracted and sorbent samples were eluted, all with ethyl acetate. The samples were analysed with liquid chromatography tandem mass spectrometry. (2)H(6)-labelled cyclophosphamide was used as an internal standard. The between-day precision was 2-5% for wipe samples, 4-6% for sorbent samples and 3-8% for filter samples. The limit of detection was 0.02 ng CP per sample for the wipe and filter methods and 0.03 ng CP per sample for the solid sorbent method. Wipe sampling on surfaces made of different materials resulted in mean recoveries between 78-106%. The desorption recovery was between 97-102% for the wipe samples, 97% for the sorbent samples and 101% for the filter samples. Samples were stable for up to 2 months at 5 degrees C and -20 degrees C and for about 2 d at room temperature. The developed methods were applied to the measurement of contamination with CP in a hospital pharmacy. Trace amounts of CP, 1.3 and 1.4 ng, were detected on surfaces in the pharmacy.     

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.     

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.