An Italian Study on Cryptosporidium and Giardia in Wastewater, Fresh Water and Treated Water

Cryptosporidium oocysts and Giardia cysts has been isolated from waters worldwide. In Italy, studies on these parasites in the environment are still limited due to absence of epidemiological evidence and difficulty of adequate methodologies of sampling and analysis. The new Drinking Water Directive 98/83/CE states that Cryptosporidium has to be determined in water intended for human consumption if Clostridium perfringens is detected. This investigation contributes to the knowledge of both Cryptosporidium and Giardia occurrences in Italian aquatic environments through a two-year monitoring program. In addition, indicator bacteria and Clostridium perfringens were monitored in sewage, surface waters, drinking water, and swimming pools and possible correlations were calculated among all the selected parameters. Cysts and oocysts were detected in sewage and surface water and Giardia numbers always prevailed over Cryptosporidium. The parasites were not detected in drinking water, while a positive sample was obtained from the analysis of swimming pools. Pearsons correlation coefficients evidenced a reciprocal correlation between both the protozoa and the Enterococci counts.     

ASSESSMENT OF POTENTIAL ATMOSPHERIC TRANSPORT AND DEPOSITION PATTERNS DUE TO RUSSIAN PACIFIC FLEET OPERATIONS

A probabilistic analysis of atmospheric transport and deposition patterns from two nuclear risk sites-Kamchatka and Vladivostok-situated in the Russian Far East to countries and geographical regions of interest (Japan, China, North and South Koreas, territories of the Russian Far East, State of Alaska, and Aleutian Chain Islands, US) was performed. The main questions addressed were the following: Which geographical territories are at the highest risk from hypothetical releases at these sites? What are the probabilities for radionuclide atmospheric transport and deposition on different neighboring countries in case of accidents at the sites? For analysis, several research tools developed within the Arctic Risk Project were applied: (1) isentropic trajectory model to calculate a multiyear dataset of 5-day forward trajectories that originated over the site locations at various altitudes; (2) DERMA long-range transport model to simulate 5-day atmospheric transport, dispersion, and deposition of 137Cs for 1-day release (at the rate of 10(10) Bq/s); and (3) a set of statistical methods (including exploratory, cluster, and probability fields analyses) for evaluation of trajectory and dispersion modeling results. The possible impact (on annual, seasonal, and monthly basis) of selected risk sites on neighboring geographical regions is evaluated using a set of various indicators. For trajectory modeling, the indicators examined are: (1) atmospheric transport pathways, (2) airflow probability fields, (3) fast transport probability fields, (4) maximum possible impact zone, (5) maximum reaching distance, and (6) typical transport time fields. For dispersion modeling, the indicators examined are: (1) time integrated air concentration, (2) dry deposition, and (3) wet deposition. It was found for both sites that within the boundary layer the westerly flows are dominant throughout the year (more than 60% of the time), increasing with altitude of free troposphere up to 85% of the time. For the Kamchatka site, the US regions are at the highest risk with the average times of atmospheric transport ranging from 3 to 5.1 days and depositions of 10(-1) Bq/m2 and lower. For the Vladivostok site, the northern China and Japan regions are at the highest risk with the average times of atmospheric transport of 0.5 and 1.6 days, respectively, and depositions ranging from 10(0) to 10(+2) Bq/m2. The areas of maximum potentially impacted zones are 30 x 10(4) km2 and 25 x 10(4) km2 for the Kamchatka and Vladivostok sites, respectively.     

Stream Temperature Modeling Using Functional Regression Models

Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual curve of temperatures as one entity, rather than multiple daily or weekly values in classical models. Three types of functional models were explored in the study and compared to two classical models (Generalized Additive Model and Logistic Model) for six rivers from the United States The results show the functional models have the best performance for all the considered rivers. When comparing functional models between them, one variant of the historical functional model performs better than the two other models and is the most parsimonious. Functional regression leads to encouraging results to model the complete annual stream temperature curve as one entity compared to other classical approaches.     

Income inequality and willingness to pay for environmental public goods

We study how the distribution of income among members of society, and income inequality in particular, affects social willingness to pay (WTP) for environmental public goods. We find that social WTP for environmental goods decreases (increases) with income inequality if and only if environmental goods and manufactured goods are substitutes (complements). We derive adjustment factors for benefit transfer to control for differences in income distributions between a study site and a policy site. For illustration, we quantify how social WTP for environmental public goods depends on the respective income distributions for empirical case studies in Sweden and the World at large. We find that the adjustment for income inequality can be substantial.     

Putting People on the Map through an Approach That Integrates Social Data in Conservation Planning

Conservation planning is integral to strategic and effective operations of conservation organizations. Drawing upon biological sciences, conservation planning has historically made limited use of social data. We offer an approach for integrating data on social well‐being into conservation planning that captures and places into context the spatial patterns and trends in human needs and capacities. This hierarchical approach provides a nested framework for characterizing and mapping data on social well‐being in 5 domains: economic well‐being, health, political empowerment, education, and culture. These 5 domains each have multiple attributes; each attribute may be characterized by one or more indicators. Through existing or novel data that display spatial and temporal heterogeneity in social well‐being, conservation scientists, planners, and decision makers may measure, benchmark, map, and integrate these data within conservation planning processes. Selecting indicators and integrating these data into conservation planning is an iterative, participatory process tailored to the local context and planning goals. Social well‐being data complement biophysical and threat‐oriented social data within conservation planning processes to inform decisions regarding where and how to conserve biodiversity, provide a structure for exploring socioecological relationships, and to foster adaptive management. Building upon existing conservation planning methods and insights from multiple disciplines, this approach to putting people on the map can readily merge with current planning practices to facilitate more rigorous decision making. Poner a la Gente en el Mapa por Medio de una Estrategia que Integra Información Social en la Planeación de la Conservación     

Recycling flows in emergy evaluation: A mathematical paradox?

This paper is a contribution to the emergy evaluation of systems involving recycling or reuse of waste. If waste exergy (its residual usefulness) is not negligible, wastes could serve as input to another process or be recycled. In cases of continuous waste recycle or reuse, what then is the role of emergy? Emergy is carried by matter and its value is shown to be the product of specific energy with mass flow rate and its transformity. This transformity (τ) given as the ratio of the total emergy input and the useful available energy in the product (exergy) is commonly calculated over a specific period of time (usually yearly) which makes transformity a time dependent factor. Assuming a process in which a part of the non-renewable input is an output (waste) from a previous system, for the waste to be reused, an emergy investment is needed. The transformity of the reused or recycled material should be calculated based on the pathway of the reused material at a certain time (T) which results in a specific transformity value (τ). In case of a second recycle of the same material that had undergone the previous recycle, the material pathway has a new time (T + T₁) which results in a transformity value (τ₁). Recycling flows as in the case of feedback is a dynamic process and as such the process introduces its own time period depending on its pathway which has to be considered in emergy evaluations. Through the inspiration of previous emergy studies, authors have tried to develop formulae which could be used in such cases of continuous recycling of material in this paper. The developed approach is then applied to a case study to give the reader a better understanding of the concept. As a result, a ‘factor’ is introduced which could be included on emergy evaluation tables to account for subsequent transformity changes in multiple recycling. This factor can be used to solve the difficulties in evaluating aggregated systems, serve as a correction factor to up-level such models keeping the correct evaluation and also solve problems of memory loss in emergy evaluation. The discussion deals with the questions; is it a pure mathematical paradox in the rules of emergy? Is it consistent with previous work? What were the previous solutions to avoid the cumulative problem in a reuse? What are the consequences?     

Quantifying the Indicator Power of an Indicator Species

Abstract:  Biodiversity indicator species are needed for classifying biotopes and sites for conservation, and a number of methods have been developed for determining indicator species for this purpose. Nevertheless, in addition to site classification, there is sometimes a need to define an indicator species that indicates the occurrence of another species. For example, when a species of interest (target species) is difficult to detect or identify, a reliable indicator species can function as a tool that saves time and money. We derived a method that provides a quantitative measure of the indicator power (IP) of an indicator species for the target species or any species assemblage. We calculated the measure of IP from a presence–absence matrix that covered several sites. The method provided a list of indicator species, the presence of which reliably indicated the presence of another species (e.g., a threatened or rare species in a given area). The IP of the species was highest when the number of shared occurrences between the indicator species and the target species was high and, simultaneously, when the indicator species and the target species occurred separately in only a few cases. The IP was also positively influenced by the number of sites with no occurrences of either the indicator or the target species. Our method can also be used to quantify different types of species occurrence indications. We refer to these types as presence–presence, presence–absence, absence–presence, and absence–absence indications. To clarify the use of the method, we examined the situation with red-listed polypores in White-backed Woodpecker (Dendrocopos leucotos) habitats in Fennoscandia and found some suitable indicator species. Our method provides a new, objective way to evaluate the IP of an indicator species.     

Mapping human vulnerability and risk due to chemical accidents

Chemical accidents in the vicinity of densely populated areas can cause colossal damage. Close proximity of chemical facilities to the general public has been identified as a major issue for increased human exposure in 43% of the accidents investigated by the U.S. Chemical Safety Board (CSB). This emphasises the need for incorporating societal factors in risk assessment to plan actions in order to minimise exposure during accidents. The purpose of this research is to develop a model for the assessment of human vulnerability and risk due to chemical accidents. A GIS based methodology is proposed which uses computer aided hazard modelling tools and technical guidelines to model accidents and assesses population vulnerability. The population vulnerability is determined based on a set of societal indicators derived from relevant research work, expert opinions and suggestions by World Bank. Risk is defined as the probable magnitude of harm to humans and dependent on both the degrees of hazard and vulnerability. A case study is carried out by applying the methodology to Meghnaghat Industrial Area in Bangladesh. Accident scenarios are built and hazard modelling software ALOHA is used to spatially display accident footprints. Vulnerability of population is assessed using data from Bangladesh Bureau of Statistics (BBS) and field survey. The hazard footprints and vulnerability map are superimposed using mapping software ArcGIS to generate a composite risk map. The risk map is used to assess existing land use and recommendations are made for future land use planning. The composite risk map is expected to be of help for effective community response, emergency response planning and allocation of medical and support services during emergencies.     

黄土高原小流域雨水资源化综合效益评价体系研究

针对黄土高原地区雨水资源开发利用现状和小流域雨水资源化特点,以小流域作为研究单元,以雨水资源作为研究对象,借鉴层次分析法(AHP)原理,遵循科学、实用及其简明的原则,构建小流域雨水资源化综合效益评价数学模型,提出了综合效益评价的经济效益、生态效益和社会效益等32项评价指标。并以陕西省淳化县泥河沟小流域为例,利用所建模型进行了综合评价。评价结果可为黄土高原雨水资源高效利用和生态环境健康发展提供理论依据。