Artificial light at night (ALAN) is currently recognised as an important environmental disturbance that influences habitats, fitness and behaviour of numerous organisms. However, its effect on bird community distribution on a large spatial scale still remains unclear. Therefore, I decided to use a predictive approach to test an assumption that artificial nightlight, as one of 73 predictors, determines taxonomic, functional and phylogenetic levels of an avian community. In order to safeguard inference from any inconsistency, I used not one but four indices describing functional diversity, two measures showing phylogenetic species richness, and one reflecting taxonomic diversity. For all these measures of species communities I developed two sets of Random Forest models one set included ALAN as an additional predictor, while the other did not. Following cross validation tests as well as an independent evaluation of models, I demonstrated that artificial night light improved the performance of predictive models. Taxonomic species richness decreased linearly along with increasing artificial luminescence. Moreover, functional diversity showed a unimodal relation to ALAN, which meant that most niches were occupied on a moderate level of artificial lighting. Finally, phylogenetic diversity was under the highest pressure of ALAN, because even a minimal amount of artificial night lighting radically reduced this measure of biodiversity. On the basis of predictive maps, I also found that models which did not include urbanisation processes showed high values of avian biodiversity in regions where in fact they were low. Thus, I conclude that ALAN as a human footprint can play a key role when analysing the distribution of bird communities on large spatial scales.Pharmaceuticals and personal care products (PPCPs) cause ongoing water pollution and consequently have attracted wide attention. Constructed wetlands (CWs) show good PPCP removal performance through combined processes of substrates, plants, and microorganisms; however, most published research focuses on the role of substrates and microorganisms. This review summarizes the direct and indirect roles of wetland plants in PPCP removal, respectively. These direct effects include PPCP precipitation on root surface iron plaque, and direct absorption and degradation by plants. Indirect effects, which appear more significant than direct effects, include enhancement of PPCP removal through improved rhizosphere microbial activities (more than twice as much as bulk soil) stimulated by radial oxygen loss and exudate secretions, and the formation of supramolecular ensembles from PPCPs and humic acids from decaying plant materials which improving PPCPs removal efficiency by up to four times. To clarify the internal mechanisms of PPCP removal by plants in CWs, factors affecting wetland plant performance were reviewed. Based on this review, future research needs have been identified.The fuels retrieved from renewable sources which are usually employed as both carbon and energy sources are termed as neutral based biofuels. The most promising feedstock from renewable sources with great potentiality in contributing to the inclining energy demand is microalgae. These microalgae can be harnessed readily in terms of obtaining qualitative biodiesel with greater energy consumption under limited operational cost. The process of harvesting or dewatering microalgae could be carried under single or sequential combinations of operations. The major drawback of harvesting such as huge operational cost could be lowered by increasing the level of automation than cost of investments. The present review concentrates and explores on the techno-economic analysis of the microalgal harvesting and dewatering processes on a large scale. Along with these advanced techniques enclosing the utilization of nanoparticles for harvesting has also been explored. And it also adds with the impacts of concerning facts on energy consumption, processing cost and recovery of resources during harvesting.Indoor radon is considered as an indoor air pollutant due to its carcinogenic effect. Since the main source of indoor radon is the ground beneath the house, we utilize the geogenic radon potential (GRP) and a geogenic radon hazard index (GRHI) for predicting the geogenic component of the indoor Rn hazard in Germany. For this purpose, we link indoor radon data (n = 44,629) to maps of GRP and GRHI and fit logistic regression models to calculate the probabilities that indoor Rn exceeds thresholds of 100 Bq/m3 and 300 Bq/m3. The estimated probability was averaged for every municipality by considering only the estimates within the built-up area. Finally, the mean exceedance probability per municipality was coupled with the respective residential building stock for estimating the number of buildings with indoor Rn above 100 Bq/m3 and 300 Bq/m3 for each municipality. We found that (1) GRHI is a better predictor than GRP for indoor radon hazard in Germany, (2) the estimated number of buildings above 100 Bq/m3 and 300 Bq/m3 in Germany is ~2 million (11.6% of all residential buildings) and ~ 350,000 (1.9%), respectively, (3) areas where 300 Bq/m3 exceedance is greater than 10% comprise only 0.8% of the German building stock but 6.3% of buildings with indoor Rn exceeding 300 Bq/m3, and (4) most urban areas and, hence, most buildings (77%) are located in low hazard regions. The implications for Rn protection are twofold (1) the Rn priority area concept is cost-efficient in a sense that it allows to find the most buildings that exceed a threshold concentration with a given amount of resources, and (2) for an optimal reduction of lung cancer risk areas outside of Rn priority areas must be addressed since most hazardous indoor Rn concentrations occur in low to medium hazard areas.Human activities put stress on our oceans and with a growing global population, the impact is increasing. Stressors rarely act in isolation, with the majority of marine areas being impacted by multiple, concurrent stressors. Marine spatial cumulative impact assessments attempt to estimate the collective impact of multiple stressors on marine environments. However, this is difficult given how stressors interact with one another, and the variable response of ecosystems. As a result, assumptions and generalisations are required when attempting to model cumulative impacts. One fundamental assumption of the most commonly applied, semi-quantitative cumulative impact assessment method is that a change in modelled cumulative impact is correlated with a change in ecosystem condition. However, this assumption has rarely been validated with empirical data. We tested this assumption using a case study of seagrass in a large, inverse estuary in South Australia (Spencer Gulf). see more We compared three different seagrass condition indices, based on survey data collected in the field, to scores from a spatial cumulative impact model for the study area.