Source and pathway analysis of lead and polycyclic aromatic hydrocarbons in Lisbon urban soils.

One hundred soil samples were collected from urban spaces, in Lisbon, Portugal, in two surveys that were carried out in consecutive years, to assess the potential adverse human health effects following exposure to potentially toxic elements and organic compounds in the urban soils. The study hereby described follows on from the earlier work of the authors and aims at performing a source-pathway-fate analysis of lead (Pb) and polycyclic aromatic hydrocarbons (PAHs) in the urban soils in order to increase current knowledge on factors influencing exposure of the population. Various techniques were combined to achieve the proposed goal. Geogenic and anthropogenic sources were apportioned by means of Pb isotope mixing models. Isotope data was further coupled with geographic information system mapping to assess local mixed sources of Pb and PAHs. Unleaded vehicle exhaust and cement production show the largest relative contribution to the total soil-Pb, but their respective importance depends on factors such as location and urban landscape. The primary sources of PAHs to the urban soils are probably air and land traffic. Multivariate analysis was used to investigate which soil properties could influence mobility and fate of the contaminants. Whilst principal components analysis indicates carbonates and other calcium phases as probable factors controlling the dispersion of Pb in the urban soils, the linear models obtained from stepwise multiple regression analysis show that soil phosphorous (P) and manganese (Mn) are good predictors of the total soil Pb content. No robust model was obtained for the PAHs, impeding identifying environmental factors most likely to influence their dispersion in the urban soils. The solid-phase distribution study provided critical information to untangle the, at a first glance, contradictory results obtained by the multivariate analysis. Carbonates and other calcium phases, having these a probable anthropogenic origin, are soil components containing major fractions of Pb, P, and Mn.

A coregionalization model can assist specification of Geographically Weighted Poisson Regression: Application to an ecological study.

The geographical distribution of health outcomes is influenced by socio-economic and environmental factors operating on different spatial scales. Geographical variations in relationships can be revealed with semi-parametric Geographically Weighted Poisson Regression (sGWPR), a model that can combine both geographically varying and geographically constant parameters. To decide whether a parameter should vary geographically, two models are compared: one in which all parameters are allowed to vary geographically and one in which all except the parameter being evaluated are allowed to vary geographically. The model with the lower corrected Akaike Information Criterion (AICc) is selected. Delivering model selection exclusively according to the AICc might hide important details in spatial variations of associations. We propose assisting the decision by using a Linear Model of Coregionalization (LMC). Here we show how LMC can refine sGWPR on ecological associations between socio-economic and environmental variables and low birth weight outcomes in the west-north-central region of Portugal.

Optimization of wet shaking table process using response surface methodology applied to the separation of copper and aluminum from the fine fraction of shredder ELVs.

With the purpose of reducing the waste generated by end-of-life vehicles (ELVs) by enhancing the recovery and recycling of nonferrous metals, an experimental study was conducted with the finest size fraction of nonferrous stream produced at an ELV shredder plant. The aim of this work was to characterize the nonferrous stream and to evaluate the efficiency of a gravity concentration process in separating light and heavy nonferrous metal particles that could be easily integrated in a ELV shredder plant (in this case study the separation explicitly addressed copper and aluminum separation). The characterization of a sample of the 0-10mm particle size fraction showed a mixture of nonferrous metals with a certain degree of impurity due to the present of contaminants such as plastics. The majority of the particles exhibited a wire shape, preventing an efficient separation of materials without prior fragmentation. The gravity concentration process selected for this study was the wet shaking table and three operating parameters of the equipment were manipulated. A full factorial design in combination with a central composite design was employed to model metals recovery. Two second order polynomial equations were successfully fitted to describe the process and predict the recovery of copper and aluminum in Cu concentrate under the conditions of the present study. The optimum conditions were determined to be 11.1° of inclination, 2.8L/min of feed water flow and 4.9L/min of wash water flow. All three final products of the wet shaking table had a content higher than 90% in relation to one of the metals, wherein a Cu concentrate product was obtained with a Cu content of 96%, and 78% of Cu recovery and 2% of Al recovery.

Spatio-temporal patterns of Cu contamination in mosses using geostatistical estimation.

Several recent studies have reported temporal trends in metal contamination in mosses, but such assessments did not evaluate uncertainty in temporal changes, therefore providing weak statistical support for time comparisons. Furthermore, levels of contaminants in the environment change in both space and time, requiring space-time modelling methods for map estimation. We propose an indicator of spatial and temporal variation based on space-time estimation by indicator kriging, where uncertainty at each location is estimated from the local distribution function, thereby calculating variability intervals for comparison between several biomonitoring dates. This approach was exemplified using copper concentrations in mosses from four Portuguese surveys (1992, 1997, 2002 and 2006). Using this approach, we identified a general decrease in copper contamination, but spatial patterns were not uniform, and from the uncertainty intervals, changes could not be considered significant in the majority of the study area.