Impact of earthworms on trace element solubility in contaminated mine soils amended with green waste compost

The common practice of remediating metal contaminated mine soils with compost can reduce metal mobility and promote revegetation, but the effect of introduced or colonising earthworms on metal solubility is largely unknown. We amended soils from an As/Cu (1150 mgAs kg−1 and 362 mgCu kg−1) and Pb/Zn mine (4550 mgPb kg−1 and 908 mgZn kg−1) with 0, 5, 10, 15 and 20% compost and then introduced Lumbricus terrestris. Porewater was sampled and soil extracted with water to determine trace element solubility, pH and soluble organic carbon. Compost reduced Cu, Pb and Zn, but increased As solubility. Earthworms decreased water soluble Cu and As but increased Pb and Zn in porewater. The effect of the earthworms decreased with increasing compost amendment. The impact of the compost and the earthworms on metal solubility is explained by their effect on pH and soluble organic carbon and the environmental chemistry of each element.

Perspectives on the role of UK planning in land and property markets

There is a popular view that land use planning regulations (‘planning’) is hostile to both development and the development industry. Part of the reason for the prominence of this view is the homogenising of the notion of ‘planning’ and its reduction to development control. This paper argues that panning controls in the UK are far more sophisticated and, drawing upon empirical evidence of key property interests proposes a more complex and nuanced view of planning controls that, in large part, has the support of the developers and others.

Incorporating climate change into water resources planning in England and Wales

Public water supplies in England and Wales are provided by around 25 private-sector companies, regulated by an economic regulator (Ofwat) and and environmental regulator (Environment Agency). As part of the regulatory process, companies are required periodically to review their investment needs to maintain safe and secure supplies, and this involves an assessment of the future balance between water supply and demand. The water industry and regulators have developed an agreed set of procedures for this assessment. Climate change has been incorporated into these procedures since the late 1990s, although has been included increasingly seriously over time and it has been an effective legal requirement to consider climate change since the 2003 Water Act. In the most recent assessment in 2009, companies were required explicitly to plan for a defined amount of climate change, taking into account climate change uncertainty. A “medium” climate change scenario was defined, together with “wet” and “dry” extremes, based on scenarios developed from a number of climate models. The water industry and its regulators are now gearing up to exploit the new UKCP09 probabilistic climate change projections – but these pose significant practical and conceptual challenges. This paper outlines how the procedures for incorporating climate change information into water resources planning have evolved, and explores the issues currently facing the industry in adapting to climate change.

Forecasting the future: an overview of the strategic planning of the Bournemouth and Poole conurbation

An important part of strategic planning’s purpose should be to attempt to forecast the future, not simply to belatedly respond to events, or accept the future as inevitable. This paper puts forward a conceptual approach for seeking to achieve these aims and uses the Bournemouth and Poole area in Dorset as a vehicle for applying the basic methodology. The area has been chosen because of the significant issues that it currently faces in planning terms; and its future development possibilities. In order that alternative future choices for the area – different ‘developmental trajectories’ – can be evaluated, they must be carefully and logically constructed. Four Futures for Bournemouth/Poole have been put forward; they are titled and colour-coded: Future One is Maximising Growth – Golden Prospect which seeks to achieve the highest level of economic prosperity of the area; Future Two is Incremental Growth – Solid Silver which attempts to facilitate a steady, continuing, controlled pattern of the development for the area; Future Three is Steady State – Cobalt Blue which suggests that people in the area could be more concerned with preserving their quality of life in terms of their leisure and recreation rather than increasing wealth; Future Four is Environment First – Jade Green which makes the area’s environmental protection its top priority even at the possible expense of economic prosperity. The scenarios proposed here are not sacrosanct. Nor are they simply confined to the Bournemouth and Poole area. In theory, suitably modified, they could use in a variety of different contexts. Consideration of the scenarios – wherever located - might then generate other, additional scenarios. These are called hybrids, alloys and amalgams. Likewise it might identify some of them as inappropriate or impossible. Most likely, careful consideration of the scenarios will suggest hybrid scenarios, in which features from different scenarios are combined to produce alternative or additional futures for consideration. The real issue then becomes how best to fashion such a future for the particular area under consideration

Path planning of robots in noisy workspaces using learning automata

The problem of a manipulator operating in a noisy workspace and required to move from an initial fixed position P0 to a final position Pf is considered. However, Pf is corrupted by noise, giving rise to Pˆf, which may be obtained by sensors. The use of learning automata is proposed to tackle this problem. An automaton is placed at each joint of the manipulator which moves according to the action chosen by the automaton (forward, backward, stationary) at each instant. The simultaneous reward or penalty of the automata enables avoiding any inverse kinematics computations that would be necessary if the distance of each joint from the final position had to be calculated. Three variable-structure learning algorithms are used, i.e., the discretized linear reward-penalty (DLR-P, the linear reward-penalty (LR-P ) and a nonlinear scheme. Each algorithm is separately tested with two (forward, backward) and three forward, backward, stationary) actions.