Exposure assessment modeling system

An available aquatic fate computer model, EXAMS (Exposure Assessment Modelling System, 19), provided predictions for comparison with the field-measured volatilization flux. EXAMS inputs include ... 

IMES was developed to assist in the selection and evaluation of exposure assessment models and to provide model validation and uncertainty information on various models and their applications. IMES is composed of 3 elements 1) Selection - a query system for selecting models in various environmental media, 2) Validation - a database containing validation and other information on applications of models, and 3) Uncertainty - a database demonstrating apfhieatum nl a mode uncertainty protocol. 

EPA. 1987a. Exposure analysis modeling system (EXAMSII). Center for Exposure Assessment Modeling, Ecosystems Research Division, National Exposure Research Laboratory, Office of Research and Development. U. S. Environmental Protection Agency. Athens, GA. 

Excretion.. WADME studies Exposure analysis modelling system (EXAMS) 125-6, 72,5. 130 Exposure assessment 124 8 Extraction... 

Many other tools for estimating fate and transport, natural attenuation, exposure, and risk estimation, have been integrated into the Applied Risk Assessment Modeling System (ARAMS). As with the aforementioned models, ARAMS models are available as freeware on the Web site http //el.erdc.usace.army.mil/arams/. The ARAMS system is extensive, and builds on various models and databases that are contained within the system, and also pulls information from databases in real time from the Internet. These data are handled within the ARAMS system to provide reports. User-guided interfaces provide the means for operation however, some models may require additional specific expertise. Models such as SEEM and FISHRAND can work independently or within the ARAMS system. 

On-line system. Provides support for exposure assessments of toxic substances. Includes chemical properly estimation techniques, siahsiical analysis, multi-media modeling, and graphics display (including models)... 

MESOCHEM Chemical Atmospheric and Hazard Assessment System Impell Corporation Becky Cropper 300 Tristate Internat l Suite 400 Lincolnshire, IL 60069 (312) 940-2090 Software for atmospheric dispersion and chemical exposure assessment. A plume dispersion model. 

Absorption across biological membranes is often necessary for a chemical to manifest toxicity. In many cases several membranes need to be crossed and the structure of both the chemical and the membrane need to be evaluated in the process. The major routes of absorption are ingestion, inhalation, dermal and, in the case of exposures in aquatic systems, gills. Factors that influence absorption have been reviewed recently. Methods to assess absorption include in vivo, in vitro, various cellular cultures as well as modelling approaches. Solubility and permeability are barriers to absorption and guidelines have been developed to estimate the likelihood of candidate molecules being absorbed after oral administration. ... 

In all of the workshops, but especially in the FAT and Exposure Assessment workshops, the need for better understanding and model representation of soil systems, including both unsaturated and saturated zones, was evident. This included the entire range of processes shown in Table II, i.e., transport, chemical and biological transformations, and intermedia transfers by sorption/desorption and volatilization. In fact, the Exposure Assessment workshop (Level II) listed biological degradation processes as a major research priority for both soil and water systems, since current understanding in both systems must be improved for site-specific assessments. 

The European Commission s Joint Research Centre (on behalf of DG S ANCO) has started a project known as European Information System on Risks from Chemicals Released from Consumer Products/Articles (EIS-ChemRisks) (EU 2004), which is designed as a network to collect exposure data, exposure factors, exposure models, and health-related data. The overall objective is to develop tools and reference data to enable harmonized exposure assessment procedures in the EU. A toolbox has been designed to collect exposure information from four reference systems to systematically support exposure assessors in the EU ... 

Finally, the MOS should also take into account the uncertainties in the estimated exposure. For predicted exposure estimates, this requires an uncertainty analysis (Section 8.2.3) involving the determination of the uncertainty in the model output value, based on the collective uncertainty of the model input parameters. General sources of variability and uncertainty in exposure assessments are measurement errors, sampling errors, variability in natural systems and human behavior, limitations in model description, limitations in generic or indirect data, and professional judgment. 

Perhaps most easy to overlook are spatial and temporal dependencies. For example, the hydrologic component of the pesticide root zone model-exposnre analysis modeling system (PRZM-EX AMS) treats mnltiple field plots over whole watersheds as independent, nnconpled, simple, 1-dimensional flow systems. In reality, the field plots are coupled systems that exhibit complex 3-dimensional water flow and pesticide transport (US SAP 1999). These higher order processes introduce spatial dependencies that may need to be considered in the assessment. Temporal autocorrelations are also likely when assessing exposure. 

In the eighties and early nineties, the USEPA evaluated dietary risk with an analysis method known as the Dietary Risk Evaluation System (DRES) (USEPA, 1991), which was based on the USDA s 1977 to 1978 National Food Consumption Survey. Consequently, dietary exposure assessments became genetically referred to as DRES analyses. Currently, the USEPA is using the Dietary Exposure Evaluation Model (DEEM , Version 7.87) (Exponent, 2000), which allows exposure to be calculated from 1994 to 1996 CSFII along with the 1998 supplemental children s survey information. 

Numerous mechanistic studies of aluminum neurotoxicity have been performed, but the main sites of action have not been discerned as discussed in Section 2.4.2 and by Strong et al. (1996). Additional studies could help identify a single unifying mechanism that can explain and reconcile the wide variety of pathological, neurochemical, and behavioral effects of aluminum induced by oral exposure and in various model systems (e.g., intracerebral and intracistemal administration), but these kinds of studies are unlikely to better characterize neurotoxicity NOAELs and LOAELs relevant to MRL assessment. The relationship between aluminum exposure and neurotoxicity is an active area of research. 

In exposure assessments, mathematical and statistical models are often applied to represent the entire exposure process or parts of it. Models used in this sense quantitatively describe the relationship between their input parameters and the responses of the entire system (or part of it) to changes in these inputs. To a certain extent, a model is always a simplification of reality. The level of detail with which a model describes a system should be consistent with the objective of the assessment. 

Every exposure assessment should have a protocol written before its initiation. This protocol should hrst state the purpose of the exposure assessment and the model(s) used therein. It should also include the variables to be evaluated (i.e. a clearly defined assessment endpoint), the level of detail needed, how uncertainty will be addressed, and the relationship of uncertainty to the conclusions that may be drawn. Furthermore, the protocol should describe each of the other principles of practice noted below in sufficient detail so that the assessment is clearly adequate for the purpose. Similarly, exposure models used in assessments should be accompanied by adequate documentation regarding procedures for using the model, plus the minimum information that is required as input data and software references and computer system requirements. 

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