Responsibility for regional waste generation: A single region extended input-output analysis with uni-directional trade flows

The paper uses a regional input-output (IO) framework and data derived on waste generation by industry to examine regional accountability for waste generation. In addition to estimating a series of industry output-waste coefficients, the paper considers two methods for waste attribution but focuses first on one (trade endogenised linear attribution system (TELAS)) that permits a greater focus on private and public final consumption as the main exogenous driver of waste generation. Second, the paper uses a domestic technology assumption (DTA) to consider a regional ‘waste footprint’ where local consumption requirements are assumed to be met through domestic production.

Using Effluent Charges in Promoting Investment in Water Pollution Control Technology: A Model of Coordination Failure among Firms

Untreated wastewater being directly discharged into rivers is a very harmful environmental hazard that needs to be tackled urgently in many countries. In order to safeguard the river ecosystem and reduce water pollution, it is important to have an effluent charge policy that promotes the investment of wastewater treatment technology by domestic firms. This paper considers the strategic interaction between the government and the domestic firms regarding the investment in the wastewater treatment technology and the design of optimal e­ffluent charge policy that should be implemented. In this model, the higher is the proportion of non-investing firms, the higher would be the probability of having to incur an e­ffluent charge and the higher would be that charge. On one hand the government needs to impose a sufficiently strict policy to ensure that firms have strong incentive to invest. On the other hand, it cannot be too strict that it drives out firms which cannot afford to invest in such expensive technology. The paper analyses the factors that affect the probability of investment in this technology. It also explains the difficulty of imposing a strict environment policy in countries that have too many small firms which cannot afford to invest unless subsidised.

The Use of Passive Samplers to Reveal Industrial and Agricultural Pollution Trends in Swiss Rivers

This study shows the efficiency of passive sampling to reveal industrial and agricultural pollution trends. Two practical applications for nonpolar and polar contaminants are presented. Low-density polyethylene (LDPE) samplers were deployed for one year in the Venoge River (VD) to monitor indicator PCBs (iPCBs, IUPAC nos. 28, 52, 101, 138, 153 and 180). The results showed that the impact of PCB emissions into the river is higher in summer than in other seasons due to the low flow rate of the river during this period. P,olar organic chemical integrative samplers (POCIS) were deployed for 4 months in the Sion-Riddes canal (VS) to investigate herbicides (terbuthylazine, diuron and linuron). Desisopropylatrazine-d5 (DIA-d5) was tested as a performance reference compound (PRC) to estimate aqueous concentration. The results showed an increase of water contamination due to the studied agricultural area. The maximal contamination was observed in April and corresponds to the period of herbicide application on the crops.

Italy's urban waste metabolism

The problem of waste management is causing growing concern due to increasing generation rates, the emissions into soil, water and air, the social conflicts derived from the election of disposal sites and the loss of resources and energy among others. In this work, an innovative methodology is used to enable a better understanding of the waste generation and management system in Italy. Two new waste indicators are built to complement the conventional indicators used by official statistics. Then a multi-scale analysis of the Density of Waste Disposed (DWD) is carried out to highlight the territorial diversity of waste performances and test its contribution to detect plausible risky areas. Starting from Italian regions, the scale down goes on to the provincial level and, only for the region of Campania, the municipal one. First, the analysis shows that the DWD is able to complement the information provided by the conventional waste indicators. Second, the analysis shows the limitations of using a unique institutional solution to waste management problems. In this sense the multi-scale analysis provides with a more realistic picture of Italian waste system than using a single scale.

Health Impact Assessment and Waste Management with particular reference to Incineration: An introductory paper

This introductory brief has been written as a response to a request for information on HIA and waste management, with particular reference to incineration. EU legislation forms the basis for much of Irish waste management policy. Waste Management – Taking Stock and Moving Forward (2004) sets targets for increased prevention and minimisation, encourages reuse and gives preference to recovery and recycling, which is in line with the EU’s Sixth Environmental Action Plan (2002). In the area of waste incineration, the Waste Incineration Directive (2000/76/EC) has been transposed into Irish law and sets operating requirements for the incineration of waste.

Environmental pollution with soil-transmitted helminths in Sanliurfa, Turkey

Soil transmitted helminth (STH) infection are endemic in developing countries. A study was carried out of sewage farms, streams and vegetables to determine the sources and routes of STH infection in Sanliurfa, Turkey. Stool samples from farmhouse inhabitants as well as soil and vegetable samples from the gardens were collected and examined. In addition, water samples from streams and vegetable samples from the city market were collected and examined. One hundred and eighty-seven (59.5%) of a total of 314 samples, including 88.4% of the stool samples, 60.8% of the water samples, 84.4% of the soil samples and 14% of the vegetable samples, were found to be positive for STH eggs. These results indicate that the water, soil and vegetables are heavily contaminated, and suggest a vicious circle between humans and the environment. Improving environmental sanitation is imperative for the control of soil-transmitted helminthiasis in Sanliurfa.

Segregation Packaging and Storage Guidelines for Healthcare Risk Waste - 3rd edition

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Healthcare Waste Packaging Guidelines 2010

This document replaces the publication entitled Segregation Packaging and Storage Guidelines for Healthcare Risk Waste – 3rd edition, published in 2004. Click here to download PDF 351KB Healthcare Waste Packaging Guidelines 2010 Click here to download PDF 420KB  

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

PHE-CRCE-010: Estimating Local Mortality Burdens associated with Particulate Air Pollution

The increase in mortality risk associated with long-term exposure to particulate air pollution is one of the most important, and best-characterised, effects of air pollution on health. This report presents estimates of the size of this effect on mortality in local authority areas in the UK, building upon the attributable fractions reported as an indicator in the public health outcomes framework for England. It discusses the concepts and assumptions underlying these calculations and gives information on how such estimates can be made. The estimates are expected to be useful to health and wellbeing boards when assessing local public health priorities, as well as to others working in the field of air quality and public health. The estimates of mortality burden are based on modelled annual average concentrations of fine particulate matter (PM2.5) in each local authority area originating from human activities. Local data on the adult population and adult mortality rates is also used. Central estimates of the fraction of mortality attributable to long-term exposure to current levels of anthropogenic (human-made) particulate air pollution range from around 2.5% in some local authorities in rural areas of Scotland and Northern Ireland and between 3 and 5% in Wales, to over 8% in some London boroughs. Because of uncertainty in the increase in mortality risk associated with ambient PM2.5, the actual burdens associated with these modelled concentrations could range from approximately one-sixth to about double these figures. Thus, current levels of particulate air pollution have a considerable impact on public health. Measures to reduce levels of particulate air pollution, or to reduce exposure of the population to such pollution, are regarded as an important public health initiative.

What a waste

This poster highlights the danger associated with mixing drugs or mixing drugs and alcohol. The poster message is mixing drugs or mixing drugs and alcohol can be lethal. It also provides contact details for the National Drugs Helpline. Tel: 0800 776600.