PMC:1247194 / 11965-16397 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1247194","sourcedb":"PMC","sourceid":"1247194","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1247194","text":"Environmental Science\nEnvironmental science is the systematic study of the complex of physical, chemical, and biotic factors that act upon on an organism or an ecologic community and ultimately determine its form and survival. It can include circumstances, objects, or conditions by which an organism or community is surrounded and the aggregate of social and cultural conditions that influence the life of an individual or community. Fundamental elements of environmental science relevant to GIS applications in exposure assessment include measurement data and predictive algorithms for fate and transport of chemical compounds in the environment.\nEnvironmental science studies rely heavily on measurement data of the factors that influence life. Institutions in almost every country in the world, such as the U.S. Environmental Protection Agency (U.S. EPA), have been established with a primary mission of collecting and analyzing environmental samples to understand the impact of these factors on the health of the earth’s ecosystem. As a result, an abundance of measurement data concerning the chemical composition of air and water resources is available to environmental epidemiology studies. A basic principle in environmental sciences is that measurement data should be used within the bounds of the purpose for which the sample was collected. Often this purpose is to define regional or systematic trends in environmental quality at a scale and resolution that may not be adequate for epidemiologic studies, especially studies of individuals. For example, public water utilities operating in the United States with a service population \u003e 10,000 are required by federal law to report levels of certain byproducts of the disinfection process to the U.S. EPA. Most utilities meet this requirement by taking four samples at different locations in their water distribution system every 3 months. Although this sampling design may be sufficient to indicate compliance with the law, it may not be sufficient to adequately encompass the spatial and temporal variability in exposure necessary to classify exposure to individuals using the water.\nEnvironmental scientists often use computer-based simulation models to supplement measurement data in environmental studies. These models are generally composed of mathematic algorithms designed to predict interaction between, and effect of the complex factors on, an organism or ecologic community. The models can be stochastic (based on statistical probability) or deterministic (based on physical processes). In either case the models are dependent on measurement data for calibration of the predictive algorithms and validation of the predicted results. A fundamental rule in environmental modeling is not to transfer use of a model from one geographic region to another without validating it with measurement data from the new study area. Often such model transfer will require recalibration of the model as well. It is also a general rule in environmental modeling to reserve a statistically sufficient portion of available measurement data for model validation. Caution should also be employed in using a model at a spatial scale or temporal pattern for which it was not designed. A number of textbooks address environmental science and modeling (Clark 1996; Crawford-Brown 2001).\n“Geophysical plausibility” is the term we have coined for use in application of environmental science to exposure assessment for epidemiology. In simplest terms this axiom would dictate that an association between a contaminant source and exposure to an organism or ecologic community cannot exist unless there is a plausible geophysical route of transport for the contaminant between the source and the receptor. For example, assume we are conducting a study of drinking water as the sole source of exposure to a specific contaminant and a disease outcome. If a landfill is leaching the contaminant into a groundwater resource (aquifer) in our study area, but our study population has always used another water supply source with no geophysical connectivity to the aquifer, it is implausible that the contaminant from the landfill is causing the adverse health outcome through a drinking water route of exposure. This axiom is particularly relevant in the use of GIS-based processing functions (e.g., kriging on measurement data) to develop exposure estimates in environmental epidemiology studies.","divisions":[{"label":"title","span":{"begin":0,"end":21}},{"label":"p","span":{"begin":22,"end":648}},{"label":"p","span":{"begin":649,"end":2144}},{"label":"p","span":{"begin":2145,"end":3332}}],"tracks":[]}