Modeling geographic and demographic variability in residential concentrations of environmental tobacco smoke using national data sets

Abstract

Despite substantial attention toward environmental tobacco smoke (ETS) exposure, previous studies have not provided adequate information to apply broadly within community-scale risk assessments. We aim to estimate residential concentrations of particulate matter (PM) from ETS in sociodemographic and geographic subpopulations in the United States for the purpose of screening-level risk assessment. We developed regression models to characterize smoking using the 2006-7 Current Population SurveyFTobacco Use Supplement, and linked these with air exchange models using the 2007 American Housing Survey. Using repeated logistic and log-linear models (n ¼ 1000), we investigated whether household variables from the 2000 United States census can predict exposure likelihood and ETS-PM concentration in exposed households. We estimated a mean ETS-PM concentration of 16 mg/m 3 among the 17% of homes with non-zero exposure (3 mg/m 3 overall), with substantial variability among homes. The highest exposure likelihood was in the South and Midwest regions, rural populations, and low-income households. Concentrations in exposed households were highest in the South and demonstrated a non-monotonic association with income, related to air exchange rate patterns. We provide estimates of ETS-PM concentration distributions for different subpopulations in the United States, providing a starting point for communities interested in characterizing aggregate and cumulative risks from indoor pollutants.

Simulating indoor concentrations of NO2 and PM2.5 in multifamily housing for use in health-based intervention modeling

“… 24‐hour daily averages of (a) NO 2 and (b) PM 2.5 contributions per source from a sample of 1000 simulated households, estimated using regression models with input characteristics typical of Boston public housing. The asterisks represent typical concentrations reported in the literature for each pollutant and source (Baxter et al., 2007; Brugge et al., 2003; Chahine et al., 2010; Dockery and Spengler, 1981; Hynes et al., 2003; Lambert et al., 1993; Levy et al., 1998; Myatt et al., 2008; Özkaynak et al., 1996; Sarnat et al., 2006; Zota et al., 2005) …”

“…Once this fact is accounted for, and given the small unit sizes in our study, these values are in general agreement with previously published simulation studies as well as values measured in field studies. Modeling data from the American Housing Survey, Chahine et al (2011) estimated a mean concentration of 16.3 lg/m 3 ETS PM (s.d. = 16.9) in households with smokers, where the daily mean number of cigarettes smoked was 14.9.…”

Development of a multidimensional housing and environmental quality index (HEQI): application to the American Housing Survey

“…In particular, the PQI and Adequacy Index failed to capture environmental risk factors like mold, cockroaches, household crowding, household fuel combustion, and higher building leakage. Moreover, although prior studies have used the AHS to characterize environmental risk factors, most have focused on single AHS items like thermal comfort [ 51 ], air exchange [ 52 ], wood combustion [ 53 ], mold [ 54 ], and pests [ 55 ]. To our knowledge, the HEQI is the first multidimensional index that captures a range of housing quality and environmental health risk factors.…”

Development of a Multidimensional Housing and Environmental Quality Index (HEQI): Application to the American Housing Survey

“…In particular, the PQI and Adequacy index underestimated approximately 61.4 million U.S. households with environmental risk factors like mold, cockroaches, household crowding, indoor combustion sources, and higher building leakage. Moreover, although prior studies have used the AHS to characterize environmental risk factors, most have focused on single AHS items like thermal comfort [41], air exchange [42], wood combustion [43], mold [44], and pests [45]. To our knowledge, the HEQI is the rst multidimensional index that captures a range of housing quality and environmental health risk factors.…”