The influence of low-frequency noise pollution on the quality of life and place in sustainable cities: a case study from northern Portugal

Discussing urban planning requires rethinking sustainability in cities and building healthy environments. Historically, some aspects of advancing the urban way of life have not been considered important in city planning. This is particularly the case where technological advances have led to conflicting land use, as with the installation of power poles and building electrical substations near residential areas. This research aims to discuss and rethink sustainability in cities, focusing on the environmental impact of low-frequency noise and electromagnetic radiation on human health. It presents data from a case study in an urban space in northern Portugal, and focuses on four guiding questions: Can power poles and power lines cause noise? Do power poles and power lines cause discomfort? Do power poles and power lines cause discomfort due to noise? Can power poles and power lines affect human health? To answer these questions, we undertook research between 2014 and 2015 that was comprised of two approaches. The first approach consisted of evaluating the noise of nine points divided into two groups â near the sourceâ (e.g., up to 50 m from power poles) and â away from the sourceâ (e.g., more than 250 m away from the source). In the second approach, noise levels were measured for 72 h in houses located up to 20 m from the source. The groups consist of residents living within the distance range specified for each group. The measurement values were compared with the proposed criteria for assessing low-frequency noise using the DEFRA Guidance (University of Salford). In the first approach, the noise caused discomfort, regardless of the group. In the second approach, the noise had fluctuating characteristics, which led us to conclude that the noise caused discomfort.

Saúde e Ambiente – estudo de caso dos impactes da exposição ao ruído de baixa frequência no bem-estar da população de Serzedelo (Guimarães)

A exposição ao ruído tem efeitos nocivos e constitui um fator de risco para a saúde humana. O principal objetivo da presente pesquisa é analisar a exposição ao ruído de baixa frequência proveniente dos postes elétricos de alta tensão em áreas residenciais, especialmente em Sezerdelo (município de Guimarães) e os impactes na saúde da população que aí reside. A metodologia utilizada para as análises do ruído ambiental foi fundamentada no documento elaborado pelo Acoustics Research Centre (DEFRA), da Universidade de Salford, intitulado Procedure for the assessment of low frequency noise complaints (2011). Os níveis de ruído de Serzedelo ultrapassam os valores de referência do critério da curva nas faixas de 50Hz e 63Hz de 1/3 de oitava em todos os pontos de medição, concretizados em 2014. Neste caso, o nível de ruído proveniente dos postes de alta tensão pode ser incomodativo e suceptível de impacter na saúde da população que reside em Serzdelo.

Highfield Campus Winter Minecraft Map

Contains data (c) Defra (OGL) and Open Street Map (CC-BY-SA)

Implications of Commons Act 2006 for the registration of land as a town or village green

This paper describes the main changes of Commons Act 2006 for the registration of land as a town or village green. The purpose of the Commons Act 2006 is to protect common land and promote sustainable farming, public access to the countryside and the interests of wildlife. The changes under s15 of the Commons Act 2006 include the additional 2-year grace period for application, discounting statutory period of closure, correction of mistakes in registers, disallowing severance of rights, voluntary registration, replacement of land in exchange and some other provisions. The transitional provision contained in s15(4) Commons Act 2006 is particularly a cause for controversy as DEFRA has indicated buildings will have to be taken down where development has gone ahead and a subsequent application to register the land as a green is successful, obliging the developer to return the land to a condition consistent with the exercise by locals of recreational rights, which sums up that it would be harder in future to develop land which has the potential to be registered as a town or village green.

A cost-benefit analysis of culling badgers to control bovine tuberculosis

Bovine tuberculosis (TB) is an important economic problem. The incidence of TB in cattle herds has steadily risen in the UK, and badgers are strongly implicated in spreading disease. Since the mid-1970s the UK government has adopted a number of badger culling strategies to attempt to reduce infection in cattle. In this report, an established model has been used to simulate TB in badgers, transmission to cattle, and control by badger culling. Costs were supplied by the UK Government's Department for Environment Food and Rural Affairs (Defra) for badger trapping and gassing. Regardless of culling intensity or area simulated, an overall reduction in the herd breakdown rate was seen. With a high culling efficacy and no social perturbation, the mean Net Present Value of a few simulated culling strategies in an "ideal world" was positive, meaning the economic benefits outweighed the costs. Further work is required before these results could be considered definitive, as it is necessary to evaluate uncertainties and simulate less than perfect conditions. (c) 2005 Elsevier Ltd. All rights reserved.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).