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U2 - LBNL-1519E ER - TY - JOUR T1 - Analyzing a database of residential air leakage in the United States JF - Atmospheric Environment Y1 - 2005/06// SP - 3445 EP - 3455 A1 - Wanyu R. Chan A1 - William W. Nazaroff A1 - Phillip N. Price A1 - Michael D. Sohn A1 - Ashok J. Gadgil KW - air leakage KW - blower door KW - fan pressurization measurements KW - infiltration AB - We analyzed more than 70,000 air leakage measurements in houses across the United States to relate leakage area—the effective size of all penetrations of the building shell—to readily available building characteristics such as building size, year built, geographic region, and various construction characteristics. After adjusting for the lack of statistical representativeness of the data, we found that the distribution of leakage area normalized by floor area is approximately lognormal. Based on a classification tree analysis, year built and floor area are the two most significant predictors of leakage area: older and smaller houses tend to have higher normalized leakage areas than newer and larger ones. Multivariate regressions of normalized leakage are presented with respect to these two factors for three house classifications: low-income households, energy program houses, and conventional houses. We demonstrate a method of applying the regression model to housing characteristics from the American Housing Survey to derive a leakage-area distribution for all single-family houses in the US. The air exchange rates implied by these estimates agree reasonably well with published measurements. VL - 39 IS - 19 JO - Atmospheric Environment DO - 10.1016/j.atmosenv.2005.01.062 ER - TY - JOUR T1 - Analyzing a Database of Residential Air Leakage in the United States JF - Atmospheric Environment Y1 - 2005/06// SP - 3445 EP - 3455 A1 - Wanyu R. Chan A1 - William W. Nazaroff A1 - Phillip N. Price A1 - Michael D. Sohn A1 - Ashok J. Gadgil KW - air leakage KW - blower door KW - fan pressurization measurements KW - infiltration AB - We analyzed more than 70,000 air leakage measurements in houses across the United States to relate leakage area—the effective size of all penetrations of the building shell—to readily available building characteristics such as building size, year built, geographic region, and various construction characteristics. After adjusting for the lackof statisticalrepresentativeness of the data, we found that the distribution of leakage area normalized by floor area is approximately lognormal. Based on a classification tree analysis, year built and floor area are the two most significant predictors of leakage area: older and smaller houses tend to have higher normalized leakage areas than newer and larger ones.Multivariate regressions of normalized leakage are presented with respect to these two factors for three house classifications: low-income households, energy program houses, and conventional houses. We demonstrate a method of applying the regression model to housing characteristics from the American Housing Survey to derive a leakage-area distribution for all single-family houses in the US. The air exchange rates implied by these estimates agree reasonably well with published measurements. VL - 39 IS - 19 JO - Atmospheric Environment DO - 10.1016/j.atmosenv.2005.01.062 ER - TY - CHAP T1 - Assessing uncertainties in the relationship between inhaled particle concentration, internal deposition and health effects, Chapter 9 T2 - Aerosols Handbook: Measurement, Dosimetry and Health Effects Y1 - 2005/ SP - 157 EP - 188 A1 - Phillip N. Price ED - Lev S. Ruzer ED - Naomi H. Harley AB - The question that ultimately motivates most aerosol inhalation research is: for a given inhaled atmosphere, what health effects will result in a specified population? To attempt to address this question, quantitative research on inhaled aerosols has been performed for at least fifty years (Landahl et al, 1951). The physical factors that determine particle deposition have been determined, lung morphology has been quantified (particularly for adults), models of total particle deposition have been created and validated, and a large variety of inhalation experiments have been performed. However many basic questions remain, some of which are identified by the U.S. Committee on Research Priorities for Airborne Particulate Matter (NRC 1998a) as high-priority research areas. Among these are: What are the quantitative relationships between outdoor concentrations measured at stationary monitoring stations, and actual personal exposures? What are the exposures to biologically important constituents of particulate matter that cause responses in potentially susceptible subpopulations and the general population? What is the role of physicochemical characteristics of particulate matter in causing adverse health effects? As these questions show, in spite of significant progress in all areas of aerosol research, many of the most important practical questions remain unanswered or inadequately answered.In this chapter, we discuss the sources and magnitudes of error that hinder the ability to answer basic questions concerning the health effects of inhaled aerosols. We first consider the phenomena that affect the epidemiological studies, starting with studies of residential radon and moving on to fine particle air pollution. Next we discuss the major uncertainties in physical and physiological modeling of the causal chain that leads from inhaled aerosol concentration, to deposition in the airway, to time-dependent dose (that is, the concentration of particles at a given point in the lungs as function of time), to physiological effects, and finally to health effect. JF - Aerosols Handbook: Measurement, Dosimetry and Health Effects PB - CRC Press, Boca Raton, FL ER - TY - JOUR T1 - Advice for first responders to a building during a chemical or biological attack Y1 - 2002/ A1 - Phillip N. Price A1 - William W. Delp A1 - Michael D. Sohn A1 - Tracy L. Thatcher A1 - David M. Lorenzetti A1 - Richard G. Sextro A1 - Ashok J. Gadgil A1 - Elisabeth A. Derby A1 - Sondra A. Jarvis AB - No Abstract available. ER - TY - JOUR T1 - An algorithm for real-time tomography of gas concentrations, using prior information about spatial derivatives JF - Atmospheric Environment Y1 - 2001/06// SP - 2827 EP - 2835 A1 - Phillip N. Price A1 - Marc L. Fischer A1 - Ashok J. Gadgil A1 - Richard G. Sextro KW - Air Flow KW - computed tomography KW - Concentration mapping KW - Optical remote sensing KW - pollutant dispersion AB - We present a new computed tomography method, the low third derivative (LTD) method, that is particularly suited for reconstructing the spatial distribution of gas concentrations from path-integral data for a small number of optical paths. The method finds a spatial distribution of gas concentrations that (1) has path integrals that agree with measured path integrals, and (2) has a low third spatial derivative in each direction, at every point. The trade-off between (1) and (2) is controlled by an adjustable parameter, which can be set based on analysis of the path-integral data. The method produces a set of linear equations, which can be solved with a single matrix multiplication if the constraint that all concentrations must be positive is ignored; the method is therefore extremely rapid. Analysis of experimental data from thousands of concentration distributions shows that the method works nearly as well as smooth basis function minimization (the best method previously available), yet is about 100 times faster. VL - 35 IS - 16 JO - Atmospheric Environment DO - 10.1016/S1352-2310(01)00082-6 ER - TY - JOUR T1 - Algorithm for rapid tomography of gas concentrations JF - Atmospheric Environment Y1 - 2000/ SP - 2827 EP - 2835 A1 - Phillip N. Price A1 - Marc L. Fischer A1 - Ashok J. Gadgil A1 - Richard G. Sextro AB - We present a new computed tomography method, the low third derivative (LTD) method, that is particularly suited for reconstructing the spatial distribution of gas concentrations from path-integral data for a small number of optical paths. The method finds a spatial distribution of gas concentrations that (1) has path integrals that agree with measured path integrals, and (2) has a low third spatial derivative in each direction, at every point. The trade-off between (1) and (2) is controlled by an adjustable parameter, which can be set based on analysis of the path-integral data. The method produces a set of linear equations, which can be solved with a single matrix multiplication if the constraint that all concentrations must be positive is ignored; the method is therefore extremely rapid. Analysis of experimental data from thousands of concentration distributions shows that the method works nearly as well as Smooth Basis Function Minimization (the best method previously available), yet is 100 times faster. VL - 35 U1 -3

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