Trace metals and radionuclides in macroalgae from Moroccan coastal waters

Macroalgae species Codium sp, Bangia atropurpurea, Membranoptera alata, Plocamium cartilagineum, Dictyota dichotoma, Fucus spiralis and Stypocaulon scoparia were collected from seven stations along the north coast of Morocco. Samples were analysed to determine activities of naturally occurring radionuclides ((210)Pb, U isotopes and (40)K) and concentrations of metals (Zn, Fe, Co, Cu, Ni, Mn, Pb, Cd, As and Cr) using radiometric and ICP-OES techniques, respectively. Metal concentrations were within ranges reported in the scientific literature, and concentrations of bio-essential elements were in the order Mn>Fe> Zn>Cu in all samples. Brown algae had the highest concentrations of almost all metals, and concentrations decreased in the order brown>red>green algae. With respect to radionuclides, the red alga P. cartilagineum had the highest activities of (210)Pb, in most cases an order of magnitude higher than for the green alga Codium sp. (234)U and (238)U activities in all algae samples were in the range 0.96- 7.61 and 1.16-6.14 Bq/kg dry weight, respectively. Our analyses of radionuclide activities and metal concentrations in marine macroalgae showed large differences among taxa. These results provide insights into which algal species should be used for biomonitoring programmes.     

Aggregated Versus Individual Land-Use Models: Modeling Spatial Autocorrelation to Increase Predictive Accuracy

The objective of this paper is to compare the predictive accuracy of individual and aggregated econometric models of land-use choices. We argue that modeling spatial autocorrelation is a comparative advantage of aggregated models due to the smaller number of observation and the linearity of the outcome. The question is whether modeling spatial autocorrelation in aggregated models is able to provide better predictions than individual ones. We consider a complete partition of space with four land-use classes: arable, pasture, forest, and urban. We estimate and compare the predictive accuracies of individual models at the plot level (514,074 observations) and of aggregated models at a regular 12 × 12 km grid level (3,767 observations). Our results show that modeling spatial autocorrelation allows to obtain more accurate predictions at the aggregated level when the appropriate predictors are used.     

Unintended Consequences of Environmental Policies: the Case of Set-aside and Agricultural Intensification

Environmental Modeling & Assessment - The objective of this paper is to evaluate the indirect impacts of a set-aside policy on the environment through crop intensification. We estimate a...