Search results

Over the last 15 years, all the legislation on waste management in the Republic of Macedonia has been brought in compliance with the European legislation. The major challenge in the economy, however, still happens to be the (non) implementation of the enforced laws on green economy. Major constrains in waste management practices remain to be organization of institutions and human resources; financing of services and investments; stakeholder (non) awareness; and lack of technical management in all phases from collection to final disposal of waste. It is not only that the present situation has negative impact on the public health and the environment, but it also has serious negative economic effects which consequently affects issues related to the total economic growth of the country.

The paper has a special focus on managing packaging and packaging waste and analyzes the results of the implementation of the Law of Management of Packaging and Packaging Waste which was enforced in 2009. Positive initiatives in waste management practices were undertaken by PAKOMAK, the first Macedonian company that has been holding the license for selecting and processing of packaging waste since January 2011. The company has a proactive role in promoting the importance of packaging waste and its management, with a special emphasis on projects that increase the awareness of the whole society, especially that of the young population. Some of the projects that increase the eco-awareness of young population will be presented in the paper.

This chapter reviews factors responsible for climate change, impacts of the change on animal health, zoonotic diseases, and their linkage with One-Health program.

This chapter is based on the available literature related to climate change and its effect on animal health and production from different points. The causes and change forcers of climate change, direct and indirect effects of the change on animal health management, host–pathogen–vector interaction, and zoonotic diseases are included. Inter-linkage between climate change and One-Health program are also assessed.

Beside natural causes of climatic change, greenhouse gases are increasing due to human activities, causing global climate changes which have direct and indirect animal health and production performance impacts. The direct impacts are increased ambient temperature, floods, and droughts, while the indirect are reduced availability of water and food. The change and effect also promote diseases spread, increase survival and availability of the pathogen and its intermediate vector host, responsible for distribution and prevalence of tremendous zoonotic, infectious, and vector-borne diseases. The adverse effect on the biodiversity, distribution of animals and micro flora, genetic makeup of microbials which may lead to emerging and re-emerging disease and their outbreaks make the strong linkage between climate change and One-Health.

Global climate change is receiving increasing international attention where international organizations are increasing their focus on tackling the health impacts. Thus, there is a need for parallel mitigation of climate change and animal diseases in a global form.

Most research on climate change is limited to environmental protection, however this chapter provides a nexus between climate change, animal health, livestock production, and the One-Health program for better livelihood.

It is now well-established that good water quality is associated with economic prosperity, reduced incidence on public health and the good functioning of the various ecosystems found in our environment. Water contamination is mostly related to both diffused (agricultural lands and geologic rock degradations) and point sources of pollution. Mauritius has many water resources which depend solely on precipitation for their replenishment. Water parameters which are of relevance include total dissolved solids (TDS), temperature, pH, electrical conductivity, turbidity, dissolved oxygen, dissolved and particulate organic carbon and major cations and anions. The traditional methods of analysis for these parameters are mostly using electrical and optical methods (probes and sensors in the field), while chemical titrations, Flame AAS and High-Performance Liquid Chromatography techniques are carried out in the laboratory. Image Classification techniques using neural networks can also be used to detect the presence of contaminants in water. In addition to basic water quality parameters, the field sensors range have been extended to cover important major ions and can now be integrated with Artificial Intelligence (AI)-based models for the prediction of variations in water quality to better protect human health and the environment, reduce operation costs of water and wastewater treatment plant unit processes.

Massively parallel desktop computing capabilities now well within the reach of individual academics modify the environment for posterior simulation in fundamental and potentially quite advantageous ways. But to fully exploit these benefits algorithms that conform to parallel computing environments are needed. This paper presents a sequential posterior simulator designed to operate efficiently in this context. The simulator makes fewer analytical and programming demands on investigators, and is faster, more reliable, and more complete than conventional posterior simulators. The paper extends existing sequential Monte Carlo methods and theory to provide a thorough and practical foundation for sequential posterior simulation that is well suited to massively parallel computing environments. It provides detailed recommendations on implementation, yielding an algorithm that requires only code for simulation from the prior and evaluation of prior and data densities and works well in a variety of applications representative of serious empirical work in economics and finance. The algorithm facilitates Bayesian model comparison by producing marginal likelihood approximations of unprecedented accuracy as an incidental by-product, is robust to pathological posterior distributions, and provides estimates of numerical standard error and relative numerical efficiency intrinsically. The paper concludes with an application that illustrates the potential of these simulators for applied Bayesian inference.

Sustainable atmospheric management today involves a complex set of issues arising from the deliberate or inadvertent use of the atmosphere as a repository for waste products arising from human activities. Urban pollution affects human health, building materials and vegetation. Acidic emissions and excess nutrients produce both acid rain and dry deposition that affect terrestrial, freshwater and ocean chemistry and ecosystems. The production and effects of atmospheric pollution can transcend national boundaries and thus mitigation will require cooperation on regional and global levels, as well as local action. Global pollution includes greenhouse gases and atmospheric particles which are changing the global climate and affecting human health. While technological solutions will play an important part, the large reductions in emissions necessary to achieve sustainability will involve adopting lifestyles that conserve energy and minimise pollution. These concerns were foreshadowed in the writings of Fritz Schumacher.