Biomedical subjects

Daniel A Griffith

Publications and source records attributed to Daniel A Griffith.

5 recordsLinked to original sources

PubMed2006

Mass transfer of soil indoors by track-in on footwear.

Inadvertent soil ingestion, especially by young children, can be an important route of exposure for many environmental contaminants. The introduction of exterior soil into the interior environment is a significant element of the exposure pathway. The unintentional collection of outside soil on footwear followed by subsequent deposition indoors is a principal route of soil ingress. Here we have investigated likely rates of dry and wet soil deposition on indoor hard surface flooring as a result of mass transfer from soiled footwear. In this pilot study, testing involved both single track-in events (with deposition resulting from a single progression of transfer steps) and multiple tracking actions (with deposition and dispersion resulting from repeated transfer steps). Based on soil mass recovery from the floor surface it was found that any contamination introduced by one-time track-in events was of limited spatial extent. In contrast, under repeated tracking conditions, with multiple soil incursions, widespread floor surface contamination was possible. Soil mass recovery was accomplished by brushing, by vacuum cleaner removal and by wet wiping. All the clean-up methods operated imperfectly and failed to remove all initially deposited soil. The level of floor surface soiling that resulted from the track-in tests, and the incomplete clean-up strongly suggest that under unrestricted transfer conditions rapid accumulation and dispersal of soil on indoor flooring is likely.

Dust↗

PubMed2006

Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses.

Recently, analytical approaches based on the eigenfunctions of spatial configuration matrices have been proposed in order to consider explicitly spatial predictors. The present study demonstrates the usefulness of eigenfunctions in spatial modeling applied to ecological problems and shows equivalencies of and differences between the two current implementations of this methodology. The two approaches in this category are the distance-based (DB) eigenvector maps proposed by P. Legendre and his colleagues, and spatial filtering based upon geographic connectivity matrices (i.e., topology-based; CB) developed by D. A. Griffith and his colleagues. In both cases, the goal is to create spatial predictors that can be easily incorporated into conventional regression models. One important advantage of these two approaches over any other spatial approach is that they provide a flexible tool that allows the full range of general and generalized linear modeling theory to be applied to ecological and geographical problems in the presence of nonzero spatial autocorrelation.

Animals↗

PubMed2005

A comparison of six analytical disease mapping techniques as applied to West Nile Virus in the coterminous United States.

West Nile Virus has quickly become a serious problem in the United States (US). Its extremely rapid diffusion throughout the country argues for a better understanding of its geographic dimensions. Both 2003 and 2004 percentages of deaths by numbers of reported human cases, for the 48 coterminous US states, are analyzed with a range of spatial statistical models, seeking to furnish a fuller appreciation of the variety of models available to researchers interested in analytical disease mapping. Comparative results indicate that no single spatial statistical model specification furnishes a preferred description of these data, although normal approximations appear to furnish some questionable implications. Findings also suggest several possible future research topics.

Journal Article↗

PubMed2005

Evaluation of environmental data for identification of Anopheles (Diptera: Culicidae) aquatic larval habitats in Kisumu and Malindi, Kenya.

This research evaluates the extent to which use of environmental data acquired from field and satellite surveys enhances predictions of urban mosquito counts. Mosquito larval habitats were sampled, and multispectral thermal imager (MTI) satellite data in the visible spectrum at 5-m resolution were acquired for Kisumu and Malindi, Kenya, during February and March 2001. All entomological parameters were collected from January to May 2001, June to August 2002, and June to August 2003. In a Poisson model specification, for Anopheles funestus Giles, shade was the best predictor, whereas substrate was the best predictor for Anopheles gambiae, and vegetation for Anopheles arabensis Patton. The top predictors found with a logistic regression model specification were habitat size for An. gambiae Giles, pollution for An. arabensis, and shade for An. funestus. All other coefficients for canopy, debris, habitat nature, permanency, emergent plants, algae, pollution, turbidity, organic materials, all MTI waveband frequencies, distance to the nearest house, distance to the nearest domestic animal, and all land use land cover changes were nonsignificant. MTI data at 5-m spatial resolution do not have an additional predictive value for mosquito counts when adjusted for field-based ecological data.

Animals↗

PubMed2003

Application of geostatistics to risk assessment.

Geostatistics offers two fundamental contributions to environmental contaminant exposure assessment: (1) a group of methods to quantitatively describe the spatial distribution of a pollutant and (2) the ability to improve estimates of the exposure point concentration by exploiting the geospatial information present in the data. The second contribution is particularly valuable when exposure estimates must be derived from small data sets, which is often the case in environmental risk assessment. This article addresses two topics related to the use of geostatistics in human and ecological risk assessments performed at hazardous waste sites: (1) the importance of assessing model assumptions when using geostatistics and (2) the use of geostatistics to improve estimates of the exposure point concentration (EPC) in the limited data scenario. The latter topic is approached here by comparing design-based estimators that are familiar to environmental risk assessors (e.g., Land's method) with geostatistics, a model-based estimator. In this report, we summarize the basics of spatial weighting of sample data, kriging, and geostatistical simulation. We then explore the two topics identified above in a case study, using soil lead concentration data from a Superfund site (a skeet and trap range). We also describe several areas where research is needed to advance the use of geostatistics in environmental risk assessment.

Journal Article↗