Environmental study of two significant solid samples: gravitation dust sediment and soil

In this work are presented results of the complex study of two significant solid environmental samples: gravitation dust sediments (industrial pollutants, potential source of risk elements input to soils) and soils (component of the environment, potential source of risk elements input to food web). The first phase of this study was focused on the study of the significant chemical properties (phase composition, content of organic and inorganic carbon) of the dust and soil samples. In the second phase, the fractionation analysis was used on the evaluation of the mobility of chosen risk elements (Cu, Ni, Pb, Zn) in the studied samples. The single-step extractions were applied in the order of the isolation of the element forms (fractions), with different mobilities during defined ecological conditions by utilization of the following reagents: 1 mol dm^− 3 NH₄NO₃ for isolation of the “mobile” fraction, 0.05 mol dm^− 3 ethylenediaminetetraacetic acid and 0.43 mol dm^− 3 CH₃COOH for isolation of the “mobilizable” fraction, and 2 mol dm^− 3 HNO₃ for isolation of all releasable forms. On the basis of the results obtained in this study, it is possible to state that different origins and positions of solid environmental samples in the environment reflect in different chemical properties of their matrix. The different properties of the sample matrix result in different mobilities of risk elements in these kinds of samples. The fractionation analysis with single-step extraction for isolation element fractions is the method most suitable for easy checking of environmental pollution and for evaluation of risk elements cycle in the environment.     

Evolution of road risk disparities at small-scale level: example of Belgium

PROBLEM: Road accident outcomes are traditionally analyzed at state or road network level due to a lack of aggregated data and suitable analytical methods. The aim of this paper is to demonstrate usefulness of a simple spatiotemporal modeling of road accident outcomes at small-scale geographical level. METHOD: Small-area spatiotemporal Bayesian models commonly used in epidemiological studies reveal the existence of spatial correlation in accident data and provide a mechanism to quantify its effect. The models were run for Belgium data for the period 2000-2005. Two different scale levels and two different exposure variables were considered under Bayesian hierarchical models of annual accident and fatal injury counts. The use of the conditional autoregressive (CAR) formulation of area specific relative risk and trend terms leads to more distinctive patterns of risk and its evolution. The Pearson correlation tests for relative risk rates and temporal trends allows researchers to determine the development of risk disparities in time. RESULTS: Analysis of spatial effects allowed the identification of clusters with similar risk outcomes pointing toward spatial structure in road accident outcomes and their background mechanisms. From the analysis of temporal trends, different developments in road accident and fatality rates in the three federated regions of Belgium came into light. Increasing spatial disparities in terms of fatal injury risk and decreasing spatial disparities in terms of accident risk with time were further identified. IMPACT ON INDUSTRY: The application of a space-time model to accident and fatal injury counts at a small-scale level in Belgium allowed identification of several areas with outstandingly high accident (injury) records. This could allow more efficient redistribution of resources and more efficient road safety management in Belgium.