Towards generalised reference condition models for environmental assessment: a case study on rivers in Atlantic Canada

Evaluation of the ecological status of river sites in Canada is supported by building models using the reference condition approach. However, geography, data scarcity and inter-operability constraints have frustrated attempts to monitor national-scale status and trends. This issue is particularly true in Atlantic Canada, where no ecological assessment system is currently available. Here, we present a reference condition model based on the River Invertebrate Prediction and Classification System approach with regional-scale applicability. To achieve this, we used biological monitoring data collected from wadeable streams across Atlantic Canada together with freely available, nationally consistent geographic information system (GIS) environmental data layers. For the first time, we demonstrated that it is possible to use data generated from different studies, even when collected using different sampling methods, to generate a robust predictive model. This model was successfully generated and tested using GIS-based rather than local habitat variables and showed improved performance when compared to a null model. In addition, ecological quality ratio data derived from the model responded to observed stressors in a test dataset. Implications for future large-scale implementation of river biomonitoring using a standardised approach with global application are presented.     

Urinary arsenic levels influenced by abandoned mine tailings in the Southernmost Baja California Peninsula,Mexico

Gold has been mined at San Antonio-El Triunfo, (Baja California Sur, Mexico) since the 18th century. This area has approximately 5,700 inhabitants living in the San Juan de Los Planes and El Carrizal hydrographic basins, close to more than 100 abandoned mining sites containing tailings contaminated with potentially toxic elements such as arsenic. To evaluate the arsenic exposure of humans living in the surrounding areas, urinary arsenic species, such as inorganic arsenic (iAs) and the metabolites mono-methylated (MMA) and di-methylated arsenic acids (DMA), were evaluated in 275 residents (18–84 years of age). Arsenic species in urine were analyzed by hydride generation-cryotrapping-atomic absorption spectrometry, which excludes the non-toxic forms of arsenic such as those found in seafood. Urinary samples contained a total arsenic concentration (sum of arsenical species) which ranged from 1.3 to 398.7 ng mL^?1, indicating 33 % of the inhabitants exceeded the biological exposition index (BEI = 35 ng mL^?1), the permissible limit for occupational exposure. The mean relative urinary arsenic species were 9, 11 and 80 % for iAs, MMA and DMA, respectively, in the Los Planes basin, and 17, 10 and 73 %, respectively, in the El Carrizal basin. These data indicated that environmental intervention is required to address potential health issues in this area.     

ANNOYANCE AND COMMUNITY NOISE: PSYCHOPHYSICAL MODEL OF DOSE – RESPONSE RELATIONSHIPS

There is a paucity of theory pertaining to social surveys of annoyance and community noise. A new psychophysical model addresses data concerning resident annoyance in sites exposed to various maximum noise levels and different numbers of noise events. The best fits of this model point to the relevance of response variance for understanding the results obtained with category response scales. The argument identifies a number of psychological and physical variables that may influence annoyance reactions to community noise. Most importantly, the model suggests that the truncation inherent in category scales biases the responses, and that response variability may be critical in determining the detailed shape of the dose–response function. Regulatory decisions concerning annoyance and community noise should be more valid when based on a deeper understanding of the theoretical basis of the empirical data.     

Social Network Analysis of Multi-level Linkages: A Swedish Case Study on Northern Forest-Based Sectors

Forest use in Northern Sweden is being influenced both by global trends and local situations. This results in interactions between numerous groups that may impact local forest governance. Social network analysis can here provide insight into the total pattern of positive, negative, and cross-level interactions within user group community structure (within and among groups). This study analyses interactions within selected renewable resource sectors in two northern Swedish municipalities, both with regard to whether they are positive, neutral, or negative, as well as with regard to how local actors relate to actors across levels, e.g., with regional, national, and international actors. The study illustrates that many interactions both within and outside a given sector are seen as neutral or positive, and that considerable interaction and impact are defined as national and in some cases even international. It also indicates that the impact of Sweden’s only existing Model Forest may to some extent constitute a bridge between different sectors and levels, in comparison with the interactions between sectors in a municipality where such a cooperation mechanism does not exist.