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<p>The impact of window area on the energy balance of a building was investigated by simulations in DEROB-LTH. The glazed area was varied in three types of buildings with different types of glazing and for several climates.</p><p>One low energy house was compared to a less insulated house but identical in size and layout. Three different types of glazing were used; uncoated double glazing, double glazing with one low-e coated pane and triple glazing with two low-e coated panes. Climates with variations in solar radiation, mean temperature, altitude and latitude were chosen.</p><p>The results show that if energy efficient window alternatives are chosen the flexibility of choosing the glazed area and orientation is higher. Choosing a larger area facing south resulted in a higher heating demand for uncoated double glazing in the standard house. An increased area also resulted in an increased peak load for heating for all the simulated cases. Choosing the energy efficient glazing type gave a decrease in heating demand for increased south facing glazed area in the standard house. In the low energy house the difference in heating demand between different areas was smaller than for the standard house. </p><p>An office module with two types of switchable glazing and one solar control glazing unit was used in three different climates; Stockholm, Brussels and Rome. Larger window areas increase the cooling demand but if glazing types with lower solar transmittance are used, the difference in cooling demand between different window areas decreases. An extremely large window area, however, increases the peak load both for cooling and for heating and should therefore be avoided. Energy can be saved by using switchable windows instead of solar control or in particular standard glazing.</p>

The impact of window area on the energy balance of a building was investigated by simulations in DEROB-LTH. The glazed area was varied in three types of buildings with different types of glazing and for several climates. One low energy house was compared to a less insulated house but identical in size and layout. Three different types of glazing were used; uncoated double glazing, double glazing with one low-e coated pane and triple glazing with two low-e coated panes. Climates with variations in solar radiation, mean temperature, altitude and latitude were chosen. The results show that if energy efficient window alternatives are chosen the flexibility of choosing the glazed area and orientation is higher. Choosing a larger area facing south resulted in a higher heating demand for uncoated double glazing in the standard house. An increased area also resulted in an increased peak load for heating for all the simulated cases. Choosing the energy efficient glazing type gave a decrease in heating demand for increased south facing glazed area in the standard house. In the low energy house the difference in heating demand between different areas was smaller than for the standard house. An office module with two types of switchable glazing and one solar control glazing unit was used in three different climates; Stockholm, Brussels and Rome. Larger window areas increase the cooling demand but if glazing types with lower solar transmittance are used, the difference in cooling demand between different window areas decreases. An extremely large window area, however, increases the peak load both for cooling and for heating and should therefore be avoided. Energy can be saved by using switchable windows instead of solar control or in particular standard glazing.

Soil is one of the most precious resources on Earth because of its role in storing and recycling water and nutrients essential for life, providing a variety of ecosystem services. This vulnerable resource is at risk from degradation by erosion, salinity, contamination and other effects of mismanagement. Information from soil is therefore crucial for its sustainable management. While the demand for soil information is growing, the quantity of data collected in the field is reducing due to financial constraints. Digital Soil Mapping (DSM) supports the creation of geographically referenced soil databases generated by using field observations or legacy data coupled, through quantitative relationships, with environmental covariates. This enables the creation of soil maps at unexplored locations at reduced costs. The selection of an optimal scale for environmental covariates is still an unsolved issue affecting the accuracy of DSM. The overall aim of this research was to explore the effect of spatial scale alterations of environmental covariates in DSM. Three main targets were identified: assessing the impact of spatial scale alterations on classifying soil taxonomic units; investigating existing approaches from related scientific fields for the detection of scale patterns and finally enabling practitioners to find a suitable scale for environmental covariates by developing a new methodology for spatial scale analysis in DSM. Three study areas, covered by detailed reconnaissance soil survey, were identified in the Republic of Ireland. Their different pedological and geomorphological characteristics allowed to test scale behaviours across the spectrum of conditions present in the Irish landscape. The investigation started by examining the effects of scale alteration of the finest resolution environmental covariate, the Digital Elevation Model (DEM), on the classification of soil taxonomic units. Empirical approaches from related scientific fields were subsequently selected from the literature, applied to the study areas and compared with the experimental methodology. Wavelet analysis was also employed to decompose the DEMs into a series of independent components at varying scales and then used in DSM analysis of soil taxonomic units. Finally, a new multiscale methodology was developed and evaluated against the previously presented experimental results. The results obtained by the experimental methodology have proved the significant role of scale alterations in the classification accuracy of soil taxonomic units, challenging the common practice of using the finest available resolution of DEM in DSM analysis. The set of eight empirical approaches selected in the literature have been proved to have a detrimental effect on the selection of an optimal DEM scale for DSM applications. Wavelet analysis was shown effective in removing DEM sources of variation, increasing DSM model performance by spatially decomposing the DEM. Finally, my main contribution to knowledge has been developing a new multiscale methodology for DSM applications by combining a DEM segmentation technique performed by k-means clustering of local variograms parameters calculated in a moving window with an experimental methodology altering DEM scales. The newly developed multiscale methodology offers a way to significantly improve classification accuracy of soil taxonomic units in DSM. In conclusion, this research has shown that spatial scale analysis of environmental covariates significantly enhances the practice of DSM, improving overall classification accuracy of soil taxonomic units. The newly developed multiscale methodology can be successfully integrated in current DSM analysis of soil taxonomic units performed with data mining techniques, so advancing the practice of soil mapping. The future of DSM, as it successfully progresses from the early pioneering years into an established discipline, will have to include scale and in particular multiscale investigations in its methodology. DSM will have to move from a methodology of spatial data with scale to a spatial scale methodology. It is now time to consider scale as a key soil and modelling attribute in DSM.