Intra-Row Weed Control by use of Band Steaming

Disinfection of the soil by means of steaming has been a common method for eliminating weeds and fungal diseases. However, surface steaming of soil is a very energy-intensive process, and consequently, efforts have been made to develop a machine for narrow-band steaming of the soil under and around rows of cultivated plants prior to seeding. The use of this machine may achieve up to 90% energy savings, and will also reduce the amount of damage to the flora and fauna. Tests have shown that soil temperatures exceeding 70C will be needed to protect against germination of weed seeds. For band heating such a treatment in 50 cm rows requires about 5.8 GJ/ha

Urban building energy modelling for retrofit analysis under uncertainty

Urban building energy modelling (UBEM) is a growing research field that seeks to expand conventional building energy modelling to the realm of neighbourhoods, cities or even entire building stocks. The aim is to establish frameworks for analysing combined urban e˙ects rather than those of individual buildings, which city governments, utilities and other energy policy stakeholders can use to assess the current environmental impact of our buildings, and, maybe more importantly, the future e˙ects that energy renovation programmes and energy supply infrastructure changes might have. However, the task of creating reliable models of new or existing urban areas is diÿcult, as it requires an enormous amount of detailed input data – data which is rarely available. A solution to this problem is the introduction of archetype modelling, which is used to break down the building stock into a manageable subset of semantic building archetypes, for which, it is possible to characterize their parameters. It is the focus of this thesis to explore and develop new methods for stochastic archetype characterization that can enable archetype-based UBEM to be used for accurate urban-scale time series analysis.The thesis is divided into three parts. The first part acts as an introduction to case study data of the residential building stock of detached single-family houses (SFHs) in Aarhus, Denmark, which is used throughout the thesis for demonstration purposes.The second part concerns the development of methods for archetype modelling. Bayesian methods for archetype parameter calibration are presented that incorporates the variability of the underlying cluster of buildings, and correlation between parameters, to enable informed predictions of unseen buildings from the archetype under uncertainty. The capabilities of archetype-based UBEM are further widened through the introduction of dynamic building energy modelling that allows for time series analysis.The third part of the thesis is devoted to demonstrating the usefulness of the proposed archetype formulation as a building block for urban-scale applications. An exhaustive test scheme is employed to validate the predictive performance of the framework before establishing a city-scale UBEM of approx. 23,000 SFHs in Aarhus. It is used to forecast citywide heating energy use from 2017 up until 2050 under uncertainty of energy renovations and climate change.Overall, the proposed archetype-based UBEM framework promises very useful for fast, flexible and reliable urban-scale time series analysis, including forecasting the effects of energy renovation or city densification, to establish an informed basis for energy policy decision-making

Long-term retrospective analysis of mackerel spawning in the North Sea: a new time series and modeling approach to CPR data

We present a unique view of mackerel (Scomber scombrus) in the North Sea based on a new time series of larvae caught by the Continuous Plankton Recorder (CPR) survey from 1948-2005, covering the period both before and after the collapse of the North Sea stock. Hydrographic backtrack modelling suggested that the effect of advection is very limited between spawning and larvae capture in the CPR survey. Using a statistical technique not previously applied to CPR data, we then generated a larval index that accounts for both catchability as well as spatial and temporal autocorrelation. The resulting time series documents the significant decrease of spawning from before 1970 to recent depleted levels. Spatial distributions of the larvae, and thus the spawning area, showed a shift from early to recent decades, suggesting that the central North Sea is no longer as important as the areas further west and south. These results provide a consistent and unique perspective on the dynamics of mackerel in this region and can potentially resolve many of the unresolved questions about this stock.lved questions about this stoc