An analysis of factors that influence personal exposure to nitrogen oxides in residents of Richmond, Virginia

Abstract

Nitrogen oxides ( NO x ) are ubiquitous pollutants in outdoor and indoor air. However, epidemiologic studies that evaluate health effects associated with NO x commonly rely upon outdoor concentrations of NO x , nitrogen dioxide ( NO 2 ), or residence characteristics as surrogates for personal exposure. In this study, personal exposures ( 48 h ) and corresponding indoor and outdoor concentrations of nitric oxide ( NO ), NO 2 , and NO x were measured ( July -September ) in 39 adults and 9 children from 23 households in Richmond, Virginia, using Ogawa passive NO x monitors. Demographic, time -activity patterns, and household data were collected by questionnaire and used to develop exposure prediction models. Adults had higher NO 2 , NO, and NO x exposures ( means: 16, 63, and 79 ppb, respectively ) than children ( 13, 49, and 62 ppb ). Measurements taken in bedrooms ( 18, 57, and 75 ppb ) and living rooms ( 19, 65, and 84 ppb ) surpassed measurements taken outdoors ( 15, 21, and 36 ppb ). In indoor locations, NO x concentrations were influenced largely by NO, and consequently, personal exposure prediction models for NO x were reflective of models for NO. Statistical models that best predicted personal exposures included indoor measurements; outdoor measurements contributed relatively little to personal exposure. Close to 70% of the variation in personal NO 2 and NO x exposure was explained by two variable models ( bedroom NO 2 and time spent in other indoor locations; bedroom NO x and time spent in kitchen ). Given appropriate resources, measurement error in epidemiologic studies can be reduced significantly with the use of personal exposure measurements or prediction models developed from indoor measurements and survey data.

Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data

“…The 43 sites (shown in Figure 1) were distributed among 39 households throughout urban Boston, with 4 participants moving and allowing us to sample in their new home. Summary statistics of NO 2 , PM 2.5 , and EC for indoor, outdoor, and ambient concentrations (collected from a centrally located monitor) are presented in Table 1 and are comparable to those seen in other studies (Zipprich et al 2002;Brunekreef et al 2005;Meng et al 2005;Brown 2006). Average indoor concentrations of NO 2 and PM 2.5 are greater than both home-specific outdoor and ambient concentrations while indoor concentrations of EC were less than both outdoor and ambient concentrations.…”

Predictors of concentrations of nitrogen dioxide, fine particulate matter, and particle constituents inside of lower socioeconomic status urban homes

“…We also found additional information value in a dummy variable created from indoor/outdoor ratios of sulfur (AERDummy i ), which theoretically captured AERs and allowed us to better incorporate some of the principles of the mass balance model. Our measured residential indoor and outdoor concentrations, and I/O relationships are largely comparable to those seen in other studies (Zipprich et al, 2002;Brunekreef et al, 2005;Meng et al, 2005). Our findings are also in general agreement with present day models for predicting the impact of indoor sources based on integrated measurements, which identified gas appliances and cooking as important sources of NO 2 (Linaker et al, 1996;Levy et al, 1998;Rotko et al, 2001;Garcia-Algar et al, 2004) and PM (Ozkaynak et al, 1994;Brunekreef et al, 2005), respectively.…”

Predicting Personal Nitrogen Dioxide Exposure in an Elderly Population: Integrating Residential Indoor and Outdoor Measurements, Fixed-Site Ambient Pollution Concentrations, Modeled Pollutant Levels, and Time–Activity Patterns

“…Ambient concentrations predicted between 11 and 19%, suggesting that ambient sites alone did not predict personal exposures. Following a similar approach of excluding indoor/ outdoor concentrations and using more readily available data, Zipprich et al (2002) incorporated TAD data and found the best combination to predict personal NO 2 included outdoor NO 2 , open windows, time spent in other locations, and type of stove. Their best model included indoor (bedroom) NO 2 and time spent in other indoor locations.…”

“…Exposure was found to be influenced by many factors, including indoor concentrations, housing characteristics, location, seasonal factors, environmental tobacco smoke (ETS), and personal activity patterns (Monn, 2001;Zipprich et al, 2002). Improved predictions of personal exposure are often identified as combinations of factors incorporated into the equation (Levy et al, 1998;Lai et al, 2004).…”