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Annoyance due to environmental odour exposure is in many jurisdictions evaluated by a yes/no decision. Such a binary decision has been typically achieved via odour impact criteria (OIC) and, when applicable, the resultant separation distances between emission sources and residential areas. If the receptors lie inside the required separation distance, odour exposure is characterised with the potential of causing excessive annoyance. The state-of-the-art methodology to determine separation distances is based on two general steps: (i) calculation of the odour exposure (time series of ambient odour concentrations) using dispersion models and (ii) determination of separation distances through the evaluation of this odour exposure by OIC. Regarding meteorological input data, dispersion models need standard meteorological observations and/or atmospheric stability typically on an hourly basis, which requires expertise in this field. In the planning phase, and as a screening tool, an educated guess of the necessary separation distances to avoid annoyance is in some cases sufficient. Therefore, empirical equations (EQs) are in use to substitute the more time-consuming and costly application of dispersion models. Because the separation distance shape often resembles the wind distribution of a site, wind data should be included in such approaches. Otherwise, the resultant separation distance shape is simply given by a circle around the emission source. Here, an outline of selected empirical equations is given, and it is shown that only a few of them properly reflect the meteorological situation of a site. Furthermore, for three case studies, separation distances as calculated from empirical equations were compared against those from Gaussian plume and Lagrangian particle dispersion models. Overall, our results suggest that some empirical equations reach their limitation in the sense that they are not successful in capturing the inherent complexity of dispersion models. However, empirical equations, developed for Germany and Austria, have the potential to deliver reasonable results, especially if used within the conditions for which they were designed. The main advantage of empirical equations lies in the simplification of the meteorological input data and their use in a fast and straightforward approach.

Research on depression stigma is needed to gain more insight into the underlying construct and to reduce the level of stigma in the community. However, few validated measurements of depression stigma are available in the Netherlands. Therefore, this study first sought to examine the psychometric properties of the Dutch translation of the Depression Stigma Scale (DSS). Second, we examined which demographic (gender, age, education, partner status) and other variables (anxiety and knowledge of depression) are associated with personal and perceived stigma within these samples.The study population consisted of an adult convenience sample (n = 253) (study 1) and a community adult sample with elevated depressive symptoms (n = 264) (study 2). Factor structure, internal consistency, and validity were assessed. The associations between stigma, demographic variables and anxiety level were examined with regression analyses.Confirmatory factor analysis supported the validity and internal consistency of the DSS personal stigma scale. Internal consistency was sufficient (Cronbach's alpha = .70 (study 1) and .77 (study 2)). The results regarding the perceived stigma scale revealed no clear factor structure. Regression analyses showed that personal stigma was higher in younger people, those with no experience with depression, and those with lower education.This study established the validity and internal consistency of the DSS personal scale in the Netherlands, in a community sample and in people with elevated depressive symptoms. However, additional research is needed to examine the factor structure of the DSS perceived scale and its use in other samples.

The heterogeneous nature of upland hard-rock catchments in terms of geology, geomorphology, superficial deposits, soil type and land use gives rise to a range of hydrochemical characteristics in stream waters. This is further complicated by the large and often rapid changes in stream flow typical of storm events. The sources of solutes and flow pathways in hard-rock catchments are still poorly understood, in particular the role of bedrock groundwater. Spatial variations in water chemistry are presented for stream waters, soils and groundwaters in the forested Plynlimon catchment of Wales, UK. The results highlight a large degree of spatial heterogeneity in each of these systems. This has major implications for the application of end-member mixing analysis and presents serious problems for modelling in scaling up from study sites to catchment scale. However, such data provide important constraints on sources, flow pathways and residence times within individual catchment compartments, knowledge of which is essential for understanding how such catchments function. The characterisation of sub-surface waters in upland catchments requires a great deal of care during sampling as well as high spatial and temporal resolution of sampling, and further work is required to characterise the Plynlimon catchments fully. Nevertheless, the presence of an active and highly stratified groundwater system is considered important as a source of solutes and water to streams. It also provides a storage medium that is likely to make a major contribution to explaining the strongly damped rainfall Cl and d2H signals measured in the streams.