A permutation-based combination of sign tests for assessing habitat selection

The analysis of habitat selection in radio-tagged animals is approached by comparing the portions of use against the portions of availability observed for each habitat type. Since data are linearly dependent with singular variance-covariance matrices, standard multivariate statistical tests cannot be applied. To bypass the problem, compositional data analysis is customarily performed via log-ratio transform of sample observations. The procedure is criticized in this paper, emphasizing the several drawbacks which may arise from the use of compositional analysis. An alternative nonparametric solution is proposed in the framework of multiple testing. The habitat use is assessed separately for each habitat type by means of the sign test performed on the original observations. The resulting p values are combined in an overall test statistic whose significance is determined permuting sample observations. The theoretical findings of the paper are checked by simulation studies. Applications to case studies previously considered in literature are discussed.     

Potential sources and meteorological factors affecting PM<Subscript>2.5</Subscript>-bound polycyclic aromatic hydrocarbon levels in six main cities of northeastern Italy: an assessment of the related carcinogenic and mutagenic risks

A yearlong sampling campaign (2012–2013) was conducted in six major cities of the Veneto region to investigate the spatial-temporal trends and the factors affecting the polycyclic aromatic hydrocarbon (PAHs) variations and identify the local sources. Sixty samples per city were collected for analyses in every alternate month (April, June, August, October, December, and February): 10 samples per sampling site in 10 consecutive days of the months selected. Samples were ultrasonically extracted with acetonitrile and processed through high-performance liquid chromatography. Total Σ-PAH concentrations ranged from 0.19 to 70.4 ng m^?3 with a mean concentration of 11.5 ng m^?3. The mean benzo[a]pyrene (BaP) concentration reached 2.0 ng m^?3, which is two-times higher than the limit set by the European Union. BaP contributed for 17.4% to the total concentration of PAHs, which showed the same pattern across the region with maxima during cold months and minima in the warm period. In this study, PAHs showed an inverse relationship with temperature, solar radiation, wind speed, and ozone. According to this study, biomass burning for household heating and cooking, followed by gaseous PAHs absorption on particles due to low atmospheric temperature, were the main reasons for increasing PAHs concentration in winter. Health risk, evaluated as lifetime lung cancer risk (LCR), showed a potential carcinogenic risk from the airborne BaP_TEQ six-fold higher in the cold season than in the warm one. Diagnostic ratios and conditional probability functions were used to locate the sources, and results confirmed that local emission, overall domestic heating, and road transport exhausts were responsible for higher concentration rates of PAHs as well as of PM_2.5.     

Particulate matter air pollution and respiratory impact on humans and animals

Air pollution is now fully acknowledged to be a public health problem and a social issue. Particulate matter (PM) concentration has been linked with several clinical manifestations of pulmonary and cardiovascular diseases and is associated with morbidity and mortality induced by respiratory diseases both in human and animals. Current research on airborne particle-induced health effects investigates the critical characteristics of particulate matter that determine their biological effects. Scientific evidence assessed that the size of the airborne particles and their surface area determine the potential to elicit inflammatory injury, oxidative damage, and other biological effects. Thus, the present review paper aims to summarize the current evidences and findings on the effect of air pollution on lung function in both humans and animals.     

Computer image generation for job simulation: An effective approach to occupational Risk Analysis

The paper deals with the general approach and features of the computer image generation for job simulation (CIGJS) method, as specially developed to support the Job Safety Analysis (JSA) technique.Starting from some general considerations on the occupational safety and health statistical data collection in Italy, when compared to US methodology, the paper provides an overview of the CIGJS method, developed to improve the effectiveness and usability of the traditional JSA.A case study is discussed in order to highlight how CIGJS makes the use of the JSA technique possible also at the design stage (this being fundamental according to a Prevention through Design approach), and allows to control the effectiveness of the adopted countermeasures.The application of the method complies with the basic European Directives requirements both on workers’ safety and health and on machinery analysis.Furthermore, CIGJS may substantially aid workers’ education and training, to the point of being a possible simulation-training device.