Statistical modeling of crystalline silica exposure by trade in the construction industry using a database compiled from the literature

A quantitative determinants-of-exposure analysis of respirable crystalline silica (RCS) levels in the construction industry was performed using a database compiled from an extensive literature review. Statistical models were developed to predict work-shift exposure levels by trade. Monte Carlo simulation was used to recreate exposures derived from summarized measurements which were combined with single measurements for analysis. Modeling was performed using Tobit models within a multimodel inference framework, with year, sampling duration, type of environment, project purpose, project type, sampling strategy and use of exposure controls as potential predictors. 1346 RCS measurements were included in the analysis, of which 318 were non-detects and 228 were simulated from summary statistics. The model containing all the variables explained 22% of total variability. Apart from trade, sampling duration, year and strategy were the most influential predictors of RCS levels. The use of exposure controls was associated with an average decrease of 19% in exposure levels compared to none, and increased concentrations were found for industrial, demolition and renovation projects. Predicted geometric means for year 1999 were the highest for drilling rig operators (0.238 mg m(-3)) and tunnel construction workers (0.224 mg m(-3)), while the estimated exceedance fraction of the ACGIH TLV by trade ranged from 47% to 91%. The predicted geometric means in this study indicated important overexposure compared to the TLV. However, the low proportion of variability explained by the models suggests that the construction trade is only a moderate predictor of work-shift exposure levels. The impact of the different tasks performed during a work shift should also be assessed to provide better management and control of RCS exposure levels on construction sites.     

Environmental quality assessment combining sediment metal levels, biomarkers and macrobenthic communities: application to the óbidos coastal lagoon (Portugal)

Macroinvertebrate benthic communities are one of the key biological components considered for the assessment of benthic integrity in the context of the Water Framework Directive (WFD). However, under moderate contamination scenarios, the assessment of macrobenthic alterations at community level alone could be insufficient to discriminate the environmental quality of coastal and transitional waters. Keeping this in view, sediment quality of moderately contaminated sites in a coastal lagoon (óbidos lagoon, Portugal) was assessed by the combination of sediment metal levels, Carcinus maenas biomarkers (accumulated metals and oxidative stress responses) and macrobenthic communities. Two sites were selected in confined inner branches (BS and BB) and a third one in the middle lagoon (ML). The site BB presented slightly higher levels of metals in sediment but biological variables calculated for macrobenthic data were not significantly different between sites. The biotic index M-AMBI that is being applied to assess environmental quality of transitional waters in the scope of the WFD pointed either to high (site ML) or good quality status (BS and BB) in the selected sites. However, crabs from BB site presented significantly higher levels of Ni in hepatopancreas than those from ML and macrobenthic community structure was significantly different between BB and ML. Additionally, spatial differences were obtained for oxidative stress parameters suggesting that BB site presented stressors for crabs (higher GST and lower GSH_t at BB site). Factor analysis (PCA) integrating sediment contamination, biomarkers in crabs and macrobenthic data also distinguished BB site as the most environmentally disturbed. On the other hand, at ML site, some macrobenthic variables (equitability and polychaetes’ diversity) were found to be enhanced by current environmental conditions, suggesting the existence of a better sediment quality. Current results pointed to the usefulness of integrating macrobenthic community alterations with responses at organism level (bioaccumulation and biochemical endpoints) in order to increase the accuracy of environmental quality assessment in lagoon systems. Moreover, the application of different statistical methods was also found to be recommendable.     

Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches

Hyperspectral data can provide prediction of physical and chemical vegetation properties, but data handling, analysis, and interpretation still limit their use. In this study, different methods for selecting variables were compared for the analysis of on-the-ground hyperspectral signatures of wheat grown under a wide range of nitrogen supplies. Spectral signatures were recorded at the end of stem elongation, booting, and heading stages in 100 georeferenced locations, using a 512-channel portable spectroradiometer operating in the 325–1075-nm range. The following procedures were compared: (i) a heuristic combined approach including lambda-lambda R² (LL R²) model, principal component analysis (PCA), and stepwise discriminant analysis (SDA); (ii) variable importance for projection (VIP) statistics derived from partial least square (PLS) regression (PLS-VIP); and (iii) multiple linear regression (MLR) analysis through maximum R-square improvement (MAXR) and stepwise algorithms. The discriminating capability of selected wavelengths was evaluated by canonical discriminant analysis. Leaf-nitrogen concentration was quantified on samples collected at the same locations and dates and used as response variable in regressive methods. The different methods resulted in differences in the number and position of the selected wavebands. Bands extracted through regressive methods were mostly related to response variable, as shown by the importance of the visible region for PLS and stepwise. Band selection techniques can be extremely useful not only to improve the power of predictive models but also for data interpretation or sensor design.