With the aim of determining the main drivers of changes in nitrophytic and oligotrophic macro-lichen communities in an industrial region with a Mediterranean climate, we considered both land-cover types and atmospheric pollutants. We determined the relation between the abundance of nitrophytic and oligotrophic species with environmental factors considering the distance of influence of land-cover types. The results showed that oligotrophic species decreased in the proximity of artificial areas, barren land and agricultural areas, associated with higher concentrations of NO2 and Zn, and Ti, probably dust of industrial and agricultural origin. Nitrophytic species were positively related to all the mentioned land-cover types, and with higher concentrations of Fe and N. Magnesium, probably from ocean aerosols, was negatively related to oligotrophic species and positively to nitrophytic. 相似文献
Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.
Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).
Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.
Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy. 相似文献