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Land use regression (LUR) modeling has become a common method for predicting pollutant concentrations and assigning exposure estimates in epidemiological studies. However, few LUR models have been developed for metal constituents of fine particulate matter (PM) or have incorporated source-specific dispersion covariates in locations with major point sources. We developed hybrid AERMOD LUR models for PM, black carbon (BC), and steel-related PM constituents lead, manganese, iron, and zinc, using fine-scale air pollution data from 37 sites across the Pittsburgh area. These models were designed with the aim of developing exposure estimates for time periods of interest in epidemiology studies. We found that the hybrid LUR models explained greater variability in PM (R = 0.79) compared to BC (R = 0.59) and metal constituents (R = 0.34-0.55). Approximately 70% of variation in PM was attributable to temporal variance, compared to 36% for BC, and 17-26% for metals. An AERMOD dispersion covariate developed using PM industrial emissions data for 207 sources was significant in PM and BC models; all metals models contained a steel mill-specific PM emissions AERMOD term. Other significant covariates included industrial land use, commercial and industrial land use, percent impervious surface, and summed railroad length.
This article was published in the following journal.
Name: The Science of the total environment
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Techniques which study entities using their topological, geometric, or geographic properties and include the dimension of time in the analysis.
A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function.
Screening techniques first developed in yeast to identify genes encoding interacting proteins. Variations are used to evaluate interplay between proteins and other molecules. Two-hybrid techniques refer to analysis for protein-protein interactions, one-hybrid for DNA-protein interactions, three-hybrid interactions for RNA-protein interactions or ligand-based interactions. Reverse n-hybrid techniques refer to analysis for mutations or other small molecules that dissociate known interactions.
A neurosurgical procedure that removes the anterior TEMPORAL LOBE including the medial temporal structures of CEREBRAL CORTEX; AMYGDALA; HIPPOCAMPUS; and the adjacent PARAHIPPOCAMPAL GYRUS. This procedure is generally used for the treatment of intractable temporal epilepsy (EPILEPSY, TEMPORAL LOBE).
Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.