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This article deals with the assessment of methodologies for water vapor condensation determination and mold growth risk analysis on the surfaces of an organic building structure based on in-situ monitoring, carried out on the structural elements of renovated roof truss above the greenhouse “Květná zahrada” in Kroměříž. The garden including the building is signed on the UNESCO Word Heritage List for its historical and architectonical value. The monitoring was focused on hygrothermal behavior of a particular attic area during winter term 2014 - 2015. The risks of water vapor condensation and mold growth were then analyzed on the basis of data obtained by the monitoring process. These also include measurement and output data accuracy is included as well. The Results were used as recommendation for a control system of forced ventilation in the monitored attic area.

Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model. A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well.

The aim of this chapter is to explore changes in the spatial distribution of creative industries in Czechia. We focus on the post-crisis period 2009–2013 using a multi-scale perspective. The core of the empirical research is based on a unique data set compiled from an annual survey of Czech-based firms by the Czech Statistical Office in productive sectors in 2009 and 2013. These data cover employment and financial indicators such as production, value added and wages, disaggregated to particular industries according to the NACE 2-digit classification and are available for the level of municipalities with extended competencies (micro-regions). We compare metropolitan regions (further divided into urban cores and commuting hinterlands) and non-metropolitan regions from the viewpoint of the spatial distribution, sectoral structure and productivity of the creative industries (a cross-sectional analysis for 2013 and 2009–2013 changes). We ask if, and to what extent, the (post)-crisis development altered the spatial and sectoral organisation of the creative industries in Czechia.

Abstract Visual impact is one of the main factors influencing the acceptance of wind farms by the public and by the authorities. It therefore often sets the environmental and social limits of energy policy and energy use. However, the assessment of visual impacts is subjective, as is often pointed out by critics of the evaluation process. The study presented here for the first time uses accurately and objectively measurable landscape indices to directly predict the visual impact of onshore wind turbines. The method also for the first time evaluates map-based landscape indices in a panoramic simulation, and this provides a better match of visual preferences with landscape indices than the cartographic projection used until now. 400 respondents from four Central European countries (Austria, Germany, Poland and Czechia) provided an evaluation of their scenic perception of 32 different landscapes, in each case with and without wind turbines. At the same time, we analysed 12 indices characterizing the principal landscape components (relief, land cover and landscape pattern) on the basis of the 32 landscape photographs. These were further tested as predictors of visual impact. The most prominent predictors of visual impact were the Percentage of Industrial Area (including Commercial, Logistic and Mining Areas), Percentage of Forest Cover, Density of Technical Infrastructure, Number of Elevation Landmarks, and Elevation Variation. None of the three landscape pattern indices was statistically significant. On the basis of a regression model that is able to predict the potential visual impact in large areas of four Central European countries (over 830,000 km2), we present the general principles of an objectivized method for predicting the visual impact of onshore wind farms. The method makes an automatic assessment of the visual impact in large areas of entire regions or countries via a GIS analysis of Sentinel data and DEM data. This forms a good basis for both preventive evaluation and causal evaluation, and provides significant support for objectivizing the planning and decision process in order to mitigate negative environmental and social impacts of the use of wind energy.

The term ‘green economy’ has recently become a topical issue that has engaged the attention of Governments, International bodies and the media. The understanding of this concept and policy concentration is carved in various ways depending on the body that is engaged. There exist varied definitions of the ‘green economy’ with many associating it directly to agriculture since it has the ‘green’ connotation. However, despite the varied definitions, one principle that stands out most is the term “Sustainable development” or simply “sustainability. It has 3 pillars namely; social sustainability, economic and environment sustainability. Based on the in-depth of knowledge of the concept of green economy and the commitment of Governments and other international organizations, several policy documents and articles have been published on the web for global consumption. This paper uses the web mining algorithms in-built in the R programming language to mine over 402 English articles on the internet on green economy. It identifies relevant terms and patterns, reveals frequent associative words and gives a conglomerate understanding of the concept. It also brings out the most active participants in the green economic drive and sought to find if by chance any of the three pillars of sustainability would be found in the most frequent terms.

Prenatal exposure to air pollution is associated with intrauterine growth restriction and low birth weight. Gene expression changes in newborns in relation to air pollution have not been sufficiently studied. We analyzed whole genome expression in cord blood leukocytes of 202 newborns from diverse localities of the Czech Republic, differing among other factors in levels of air pollution: the district of Karvina (characterized by higher concentration of air pollutants) and Ceske Budejovice (lower air pollution levels). We aimed to identify differentially expressed genes (DEGs) and pathways in relation to locality and concentration of air pollutants. We applied the linear model to identify the specific DEGs and the correlation analysis, to investigate the relationship between the concentrations of air pollutants and gene expression data. An analysis of biochemical pathways and gene set enrichment was also performed. In general, we observed modest changes of gene expression, mostly attributed to the effect of the locality. The highest number of DEGs was found in samples from the district of Karvina. A pathway analysis revealed a deregulation of processes associated with cell growth, apoptosis or cellular homeostasis, immune response‐related processes or oxidative stress response. The association between concentrations of air pollutants and gene expression changes was weak, particularly for samples collected in Karvina. In summary, as we did not find a direct effect of exposure to air pollutants, we assume that the general differences in the environment, rather than actual concentrations of individual pollutants, represent a key factor affecting gene expression changes at delivery. Environ. Mol. Mutagen. 59:401–415, 2018. © 2018 Wiley Periodicals, Inc.

ABSTRACT Langle-Flores, A.; Ocelik, P., and Perez-Maqueo, O., 2017. The role of social networks in the sustainability transformation of Cabo Pulmo: A multiplex perspective. In: Martinez, M.L.; Taramelli, A., and Silva, R. (eds.), Coastal Resilience: Exploring the Many Challenges from Different Viewpoints. Journal of Coastal Research, Special Issue No. 77, pp. 134–142. Coconut Creek (Florida), ISSN 0749-0208. Coastal tourism is often caught in a crossfire between economic benefits, ecological impacts and social tensions. Development of large-scale resorts can reduce the provision of certain ecosystem services and threat local people's livelihoods. Social networks might influence the transitions of governance systems into new adaptive models. We focus on the role of multiplex networks in the process of sustainability transformation by examining social networks that protected a marine reserve against the construction of a large scale development. The multiplex network exhibited a structure with five blocks: ...

Sustainable development emphasizes the sustainability of natural resources and the environment as well as the development of social welfare. Under the background of resource scarcity and environmental constraints, maximizing social welfare is an inevitable choice to achieve sustainable development. Ecological Wellbeing Performance (EWP) can comprehensively reflect the efficiency of natural capital conversion into social welfare, and improving EWP is a feasible measure to achieve sustainable development. Moreover, island areas are the extremely complicated ecological-social-economic systems due to the traits that are geographical isolation, scarce resources, and frequent natural disasters, so that emphasizing the sustainability of island systems is a key step for achieving sustainable development goals. This study developed the EWP model based on the improved Three-dimensional Ecological Footprint (EF) and urban-scale Human Development Index (HDI), to synthetically evaluate and analyze the sustainable development of four major island regions in China with the data in 2017. Results found that: (1) The four regions are all in ecological deficits, being in exchange for overdraft natural capital for economic development. The EFdepth is greater than the original length 1 indicating the excessive consumption of the natural capital stock. The EFsize reflects the abundance and liquidity of regional natural capital, all the four regions are with the generally low level of flow capital utilization. Hainan with highest EFsize 0.428 is due to its comparatively sparse population density, abundant resources, and strong ecological capacity, while the relatively large population density and more restricted natural capital flows make Taiwan in the lowest value. (2) Judging from the evaluation results of the HDI, Chongming (0.796) and Hainan (0.773) high development level are relatively behind Taiwan (0.912) and Zhoushan (0.827) very high development level, for the impact of income is greater, that is, economic development in Hainan and Chongming is slightly weak. (3) Taiwan, with the highest EWP 3.646, shows the excellent natural resource utilization efficiency and sustainability, followed by Zhoushan, Chongming, and Hainan. In general, increasing HDI while reducing EF can be an ideal way to improve the efficiency of ecological resources and achieve sustainable urban development.

Abstract Constructed wetlands (CWs) have been used successfully to treat municipal wastewater. However, few studies have focused on the microbial community in CWs used for tertiary wastewater treatment. In this study, 454 high-throughput pyrosequencing was applied to analyze the bacterial community in unplanted, planted, and litter loaded CWs. Results showed that both plants and litter increased the diversity and abundance of the microbial community in CWs. The effects of plant biomass on the bacterial community at the phylum level were not pronounced, but the compositions were significantly affected at the class and genus levels. Specific comparison down to the genus level revealed that the relative abundances of nitrifier, denitrifier and sulfate-reducing bacteria were positively correlated with the removal efficiencies of ammonia, nitrate and sulfate observed. Highly functional organization (>70%) of the bacterial communities indicates that only a small group of species played a dominant role in CWs. The presence of plants and litter significantly affected the bacterial composition via alteration of carbon content (%) and pH values in the gravel matrix. The observation in this study provides useful information about the effects of plant biomass on the bacterial community structure, which plays an important role in the tertiary wastewater treatment process in CWs.