Characterization of model error in a simulation of fine particulate matter exposure distributions of the working age population in Helsinki, Finland

Exposure models are needed for comparison of scenarios resulting from alternative policy options. The reliability of models used for such purposes should be quantified by comparing model outputs in a real situation with the corresponding observed exposures. Measurement errors affect the observations, but if the distribution of these errors for single observations is known, the bias caused for the population statistics can be corrected. The current paper does this and calculates model errors for a probabilistic simulation of 48-hr fine particulate matter (PM2.5) exposures. Direct and nested microenvironment-based models are compared. The direct model requires knowledge on the distribution of the indoor concentrations, whereas the nested model calculates indoor concentrations from ambient levels, using infiltration factors and indoor sources. The model error in the mean exposure level was <0.5 microg m(-3) for both models. Relative errors in the estimated population mean were +1% and -5% for the direct and nested models, respectively. Relative errors in the estimated SD were -9% and -23%, respectively. The magnitude of these errors and the errors calculated for population percentiles indicate that the model errors would not drive general conclusions derived from these models, supporting the use of the models as a tool for evaluation of potential exposure reductions in alternative policy scenarios.     

COST EFFECTIVE OPERATION OF URBAN WATER SUPPLY SYSTEM USING DYNAMIC PROGRAMMING1

ABSTRACT: Efficient operation of a city water supply system is an important goal of all municipalities. Efficient operation should result in minimum operation cost through reduction in total energy use and/ or reduction in on-peak energy consumption. An optimization model was designed for operating the water supply systems of cities using groundwater. The Newton-Raphson pipe network was used for network analysis and a dynamic programming optimization algorithm was used for determining a schedule for pump operation in the pipe network system. The model is most suitable for use in small cities with up to 45,000 in population, but with large-scale disintegration techniques may also be used for larger cities. The savings in operation costs are a function of energy cost and energy use pattern and water use pattern in the area.     

Die Bedeutung der Immunologie für den Menschen — Eingriff und Wandel

The Science of Nature - Classical immunological research has been mainly devoted to natural defense mechanisms against infections and to the development and action of vaccines. With the discovery...     

Treatment technologies for 1,4‐Dioxane: Fundamentals and field applications

1,4‐Dioxane is a synthetic industrial chemical frequently found at contaminated sites where 1,1,1‐trichloroethane was used for degreasing. It is a probable human carcinogen and has been found in groundwater at sites throughout the United States. The physical and chemical properties and behavior of 1,4‐dioxane create challenges for its characterization and treatment. It is highly mobile and has not been shown to readily biodegrade in the environment. In December 2006, the U.S. Environmental Protection Agency's Office of Superfund Remediation and Technology Innovation (OSRTI) prepared a report titled “Treatment Technologies for 1,4‐Dioxane: Fundamentals and Field Applications.” The report provides information about the chemistry of dioxane, cleanup goals, analytical methods, available treatment technologies, and site‐specific treatment performance data. The information may be useful to project managers, technology providers, consulting engineers, and members of academia faced with addressing dioxane at cleanup sites or in drinking water supplies. This article provides a synopsis of the US EPA report, which is available at http://cluin.org/542R06009 . © 2007 Wiley Periodicals, Inc.