A comparative assessment of heavy metal accumulation in soft parts and byssus of mussels from subarctic, temperate, subtropical and tropical marine environments

Existing data on metal concentrations in mussels from subarctic, temperate, subtropical and tropical waters were analyzed using multivariate statistics in order to assess regional variations in metal contamination. Potential errors were reduced by only analyzing data from surveys that employed the same protocols, analytical methodologies and analysts. Factor analysis demonstrated that mussels inhabiting extremely contaminated areas (e.g. from Japanese and Swedish metallurgy sources) could be separated from mussels from other contaminated areas, and that metals such as Cd, Pb, Cu and Zn could be used to identify heavily contaminated samples while Co, Fe, Cr and Ni concentrations were good markers for exposure to inputs from different industrial sources. Furthermore byssus, like soft tissue, selectively and sensitively reflects variations of certain metal concentrations in ambient waters and thus serves as a reliable biomonitor for these contaminants in a variety of coastal and estuarine areas.     

State space models with spatial deformation

Space deformation has been proposed to model space-time varying observation processes with non-stationary spatial covariance structure under the hypothesis of temporal stationarity. In real applications, however, the temporal stationarity assumption is inappropriate and unrealistic. In this work we propose a spatial-temporal model whose temporal trend is modeled through state space models and a spatially varying anisotropy is modeled through spatial deformation, under the Bayesian approach. A distinctive feature of our approach is the consideration of model uncertainty in an unified framework. Our model has a clear advantage over the ones proposed so far in the literature when the main objective of the study is to perform spatial interpolation for fixed points in time. Approximations of the posterior distributions of the model parameters are obtained via Markov chain Monte Carlo methods. This allows for prediction of the process values in space and time as well as handling of missing values. Two applications are presented: the first one to model concentrations of sulfur dioxide in the eastern United States and the second one to model monthly minimum temperatures in the State of Rio de Janeiro.     

Spatial and temporal variations and mobile source emissions of polycyclic aromatic hydrocarbons in Quito, Ecuador

Motor vehicles are a major source of air pollution in Quito, Ecuador; however, little work has been done to characterize spatial and temporal variations in traffic-related pollutants, or to measure pollutants in vehicle emissions. We measured PAH continuously for one year at two residential sites in Quito, and PAH and traffic patterns for one week near a busy roadway. Morning rush-hour traffic and temperature inversions caused daily PAH maxima between 06:00 and 08:00. SO₂, NO_x, CO, and PM_2.5 behaved similarly. At the residential sites PAH levels during inversions were 2-3-fold higher than during the afternoon, and 10-16-fold higher than 02:00-03:00 when levels were lowest. In contrast, at the near-roadway site, PAH concentrations were 3-6-fold higher than at the residential sites, and the effects of inversions were less pronounced. Cars and buses accounted for >95% of PAH at the near-roadway site. Near-roadway PAH concentrations were comparable to other polluted cities.