Use of native mosses as biomonitors of heavy metals and nitrogen deposition in the surroundings of two steel works

A biomonitoring survey using the moss species Hypnum cupressiforme Hedw. was conducted in the surroundings of two steel plants located in the North of Spain. Levels of V, Cr, Ni, Cu, Zn, As, Cd, Hg, Pb and N were determined. Very high concentrations in the areas of study were detected when compared to nearby unaffected regions. Similar trends were observed for all the elements in the differently orientated transects, showing an appreciable influence of the NW prevailing winds of the region in the dispersion of pollutants, as well as a clear decreasing gradient in the concentrations of metals in mosses within a distance of 1500 meters from the facilities. A differentiation between the elements emitted by the chimney as result of the industrial activity (V, Cr, Ni, Cu and As) and those with a high presence in steel slag deposits (Zn, Cd, Hg and Pb) was observed. The range of contamination was also established by means of the Contamination Factor, indicating a category 4 out of 6 categories, which shows the high levels reported in the areas of study. A different dynamic was registered for nitrogen regarding the rest of the heavy metals analysed except for Hg, probably due to the elevated volatility and mobility of both elements, as well as their high persistence in the atmosphere.     

Advanced REACH Tool (ART): calibration of the mechanistic model

The mechanistic model of the Advanced Reach Tool (ART) provides a relative ranking of exposure levels from different scenarios. The objectives of the calibration described in this paper are threefold: to study whether the mechanistic model scores are accurately ranked in relation to exposure measurements; to enable the mechanistic model to estimate actual exposure levels rather than relative scores; and to provide a method of quantifying model uncertainty. Stringent data quality guidelines were applied to the collated data. Linear mixed effects models were used to evaluate the association between relative ART model scores and measurements. A random scenario and company component of variance were introduced to reflect the model uncertainty. Stratified analyses were conducted for different forms of exposure (abrasive dust, dust, vapours and mists). In total more than 2000 good quality measurements were available for the calibration of the mechanistic model. The calibration showed that after calibration the mechanistic model of ART was able to estimate geometric mean (GM) exposure levels with 90% confidence for a given scenario to lie within a factor between two and six of the measured GM depending upon the form of exposure.     

The development and application of remote sensing to monitor sand dune habitats

Sand dunes are complex systems that contain several habitats, often as mosaics or transitions between types. Several of these habitats are afforded protection under European Legislation and in the UK nationally within Special Areas of Conservation (SAC) and Sites of Special Scientific Interest (SSSI). Natural England has a statutory duty to report to Europe on the conservation status and condition of sand dunes; and is required to report to the UK Government on designated sites. To achieve this we have sought ways of capturing, analysing and interpreting data on the extent and location of sand dune habitats. This requires an ability to be able to obtain data over large areas of coastline in an efficient way. Natural England and Environment Agency Geomatics have worked collaboratively for over 16 years, sharing data and ecological knowledge. In 2012 work started to evaluate the use of remote sensing to map UK BAP and Annex I sand dune habitats. A methodology has now been developed and tested to map sand dune habitats. The key objective was to provide an operational tool that will help to map these habitats and understand change on sites around England. This has been achieved through analysis of LIDAR and Compact Airborne Spectrographic Imager (CASI) data using Object Orientated Image Analysis. Quality Control (QC) and accuracy assessments have shown this approach to be successful and 11 sites have been mapped to date. These techniques are providing a new approach to monitoring change in coastal vegetation communities and informing management of protected sites.     

Validation of the inhalable dust algorithm of the Advanced REACH Tool using a dataset from the pharmaceutical industry

As it is often difficult to obtain sufficient numbers of measurements to adequately characterise exposure levels, occupational exposure models may be useful tools in the exposure assessment process. This study aims to refine and validate the inhalable dust algorithm of the Advanced REACH Tool (ART) to predict airborne exposure of workers in the pharmaceutical industry. The ART was refined to reflect pharmaceutical situations. Largely task based workplace exposure data (n = 192) were collated from a multinational pharmaceutical company with exposure levels ranging from 5 × 10(-5) to 12 mg m(-3). Bias, relative bias and uncertainty around geometric mean exposure estimates were calculated for 16 exposure scenarios. For 12 of the 16 scenarios the ART geometric mean exposure estimates were lower than measured exposure levels with on average, a one-third underestimation of exposure (relative bias -32%). For 75% of the scenarios the exposure estimates were, within the 90% uncertainty factor of 4.4, as reported for the original calibration study, which may indicate more uncertainty in the ART estimates in this industry. While the uncertainty was higher than expected this is likely due to the limited number of measurements per scenario, which were largely derived from single premises.