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This paper reviews 18 surveys of mental health problems among homeless adolescents and reports on a pilot study of the same topic conducted in Amsterdam. Sampling methods and measures of mental health are discussed. The reported estimates of mental health problems vary greatly, very probably because of methodological differences. Despite the different methods used, there seems to be considerable research evidence to support a high prevalence of mental disorders among homeless adolescents. The results of the pilot study of 50 homeless adolescents in Amsterdam are consistent with the surveys reviewed. A highly structured interview was conducted at all four services sites for homeless adolescents in Amsterdam. Of the homeless adolescents interviewed, 78% had at least one lifetime DIS/DSM-III-R diagnosis, and 64% had at least one 1-month diagnosis.

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Major depression is a highly recurrent and disabling disorder. At least 60% of first depressed individuals will have another episode. Knowledge of the predictors of recurrence is crucial in advising continuation and/or maintenance treatment.Over the last 20-30 years a number of studies identified several sociodemographic-, psychosocial-, personality-, and clinical factors associated with the recurrence of major depression.This presentation will give an overview of the most important predictors associated with recurrence of depression. Relevant articles were obtained through a search in Medline, Embase, and PsycINFO with the keywords recurrence, relapse, and major depression. This search covered the period from 1980 to 2007. Criteria to select the best studies will be presented.The studies were further divided in general population studies, primary care and specialised mental health care studies.

Regional geochemical data of heavy metals are commonly used for environmental risk assessment and management. Often these data are based on so-called total concentrations, whereas the exposure to the mobile or reactive fraction of these elements finally determines whether the exposed ecosystem is at risk and to which extent. The objective of our research was to develop a wider applicable method for quantitative hazard assessment of soil metal contamination attributable to the activity of man, based on and illustrated with data from the Netherlands. Since chemical availability (0.43 M HNO3 extractable concentrations) of Cd, Cu, Pb and Zn appeared strongly related to the estimated anthropogenic enrichment, we used these concentrations to assess the hazard of human-induced enrichment of these metals. We expressed the enrichment hazard using the toxic pressure concept, which estimates the fraction of biological species (varying between 0 and 1) potentially affected due to the level of exposure to single metals or their local mixtures. This is done using logistic (enrichment) concentration/response models parameterized with ecotoxicological effect data from toxicity tests and mixture models. Hazards varied from very low toxic pressures (lower than 0.01) to (most often) toxic pressure less than 0.05, whereby the latter relates to the so-called 95%-protection criterion used in some soil protection legislations. In rare cases, the toxic pressure exceeded the value of 0.05, to an upper limit of 0.054 for Cd. The rank order of metal enrichment hazards suggests that Cd enrichment induces the largest hazard increase. There are limited (rank order) differences in enrichment hazards between soil types. Comparing the judgement of soils based on soil screening levels and based on toxic pressure of anthropogenic Cd, Cu, Pb and Zn enrichments, the soil screening values appear to more conservative. This exemplifies the use of soil screening values as a method to note regulatory concern, but not always indicating an actual hazard or risk. When screening values are exceeded, refined hazard insights can be obtained, as illustrated in this paper. This provides a more refined insight in the ecotoxic implications of human-induced metal enrichments in soils, as refined basis for risk management decisions.

Throughout recent decades, the excessive use of animal manure and fertiliser put a threat on the quality of ground and surface waters in main agricultural production areas in Europe and other parts of the world. Finding a balance between agricultural production and environmental protection is a prerequisite for sustainable development of ground and surface waters and soil quality. To protect groundwater quality, the European Commission has stipulated a limit value for NO3 − of 50 mg l−1. Member states are obliged to monitor and regulate nitrate concentrations in groundwater. In the Netherlands, this monitoring is carried out by sampling nitrate concentrations in water leaching from the root zone at farm level within the national Minerals Policy Monitoring Program. However, due to the costly procedure, only a limited number of about 450 farms can be sampled each year. While this is sufficient for providing a national overview of nitrate leaching, as a result of current and future challenges regarding the sustainable development of the agricultural system, Dutch policymakers need to gain insight into the spatial distribution of nitrate at smaller spatial scales. This study aimed to develop a predictive modelling framework to create annual maps with full national coverage of nitrate concentrations leaching from the root zone of Dutch agricultural soils, and to test this model for the year 2017. We used nitrate data from a national monitoring program and combined them with a large set of auxiliary spatial data, such as soil types, groundwater levels and crop types. We used the Random Forest (RF) algorithm as a prediction and interpolation method. Using the model, we could explain 58% of variance, and statistical errors indicate that the interpolation and map visualisation is suitable for interpretation of the spatial variability of nitrate concentrations in the Netherlands. We used the variable importance from the RF and the partial dependency of the most important variables to get more insight into the major factors explaining the spatial variability. Our study also shows the caveats of data-driven algorithms such as RF. For some areas where no training data was available, the model’s predictions are unexpected and might indicate a model bias. The combination of visualisation of the spatial variability and the interpretation of variable importance and partial dependence results in understanding which areas are more vulnerable to NO3 − leaching, in terms of land use and geomorphology. Our modelling framework can be used to target specific areas and to take more targeted regional policy measurements for the balance between agricultural production and protecting the environment.