Environ. Sci. Technol. 1998, 32, 463-468
Modeling Ozone Levels in and around Southern California Homes EDWARD L. AVOL* AND WILLIAM C. NAVIDI† Department of Preventive Medicine, University of Southern California School of Medicine CHP236, 1540 Alcazar Street, Los Angeles California 90033 STEVEN D. COLOME Integrated Environmental Services, Post Office Box 19565, Irvine California 92623
To investigate residential ozone (O3) concentrations and their relationship to regional monitoring data, we studied 126 southern California homes between February and December 1994. Using a controlled flow sampler, 481 samples were collected over 24 h sampling periods, both inside (n ) 241) and immediately outside (n ) 240) residences. Indoor O3 levels (13 ( 12 ppb, arithmetic mean) were almost always below observed outdoor measurements (37 ( 19 ppb). Low outdoor concentrations resulted in uniformly low indoor concentrations, but high outdoor levels resulted in a range of indoor levels. Indoor/outdoor ratios (0.37 ( 0.25) were greater during the summer pollution period. Using information collected from interviews performed before and after sampling, we explored relationships between measured indoor O3, home operating characteristics, and ambient O3 reported at the closest regional monitoring station. Indoor O3 levels were largely determined by outdoor O3 levels and the duration of time that windows were kept open. Ozone measured adjacent to study homes predicted indoor levels no better than station ambient values. These data suggest that ambient O3 measured at regional stations, coupled with information about how homes are operated, predict in-home O3 levels moderately well and are potentially useful for future exposure assessment purposes.
Introduction Air pollution epidemiology studies often use fixed-site outdoor monitors to assign exposures to study populations (1), based on the premise that samplers sited for regulatory compliance monitoring adequately characterize exposure of the study population. When pollutants vary gradually over a broad region and display similar outdoor and microenvironmental concentrations, these are appropriate scientific assumptions. However, if pollutant concentrations in different microenvironments vary, due to local sources, sinks, or to the reactive nature of the pollutants themselves, exposures measured by a stationary regional outdoor ambient monitor may over-report or under-report exposure in microenvironments of human exposure assessment interest. * Author to whom correspondence should be addressed. Fax: (213) 342-3272; e-mail:
[email protected]. † Current affiliation: Department of Mathematics and Computer Science, Colorado School of Mines, Golden CO 80401. S0013-936X(97)00351-9 CCC: $15.00 Published on Web 01/16/1998
1998 American Chemical Society
Assessment of the important microenvironments in which human exposures occur begins with identification of microenvironments of importance. Recent investigations have revealed that many people spend a large amount of time indoors, with much of that time spent at home (2). Statewide studies have revealed that California children may spend over 85% of their time indoors (3, 4). It is therefore critical that indoor pollutant exposures, especially those in and around the home, be considered in assessing population exposure. Ozone (O3) is a pollutant of regulatory and research interest; population exposures to it are potentially prone to misclassification because of ozone’s reactivity and concentration variability indoors. Indoor/outdoor concentration ratios can vary from near zero in residential settings using air conditioning (5, 6) to near 1, in commercial buildings with active ventilation and no filtration (7). Indoors, ozone has been shown to react with surfaces (8), volatile organics from carpets (9), and nitric oxide (10). On a regional scale, little is known about the relationship between O3 levels observed at regulatory network monitoring sites and O3 immediately surrounding and within homes. In homes having a high air exchange rate, outdoor pollution levels may well be a good predictor of indoor air quality. Alternatively, vegetation canopies and surface effects in and around residences could influence observed concentrations. To address the relationship between outdoor and indoor O3 levels in residences and to evaluate the relationship between residential exposure levels and observed O3 levels at regional monitoring fixed-site locations, we studied 126 homes during the spring, summer, and fall in southern California.
Experimental Section Field operations were performed between February and December 1994 in the greater Los Angeles metropolitan area, in four communities concurrently participating in a longitudinal health study (11). Homes were selected from a computer-generated random listing of addresses of subjects from the longitudinal study. At each residence studied, identical samplers were placed immediately outside and inside the home. Siting criteria for indoor samplers included sampling in an indoor location of central activity (usually the den or family room), locating monitoring instrumentation away from entry doors or windows, and sampling from a height of at least 1 m above the floor. Residents were encouraged to continue with normal room use activities during sampling. Siting criteria for samplers outside the homes included placement of the samplers on the rear patio or porch (for security reasons, in addition to access to electricity), sampling from at least 1 m above the floor surface, and avoidance of tree canopies, roof overhangs, or home air vents. The study sampler contained a passive O3 sampling device housed in a larger flow-controlled sampling unit. The passive O3 sampler, based on the oxidation of a nitrite-coated filter, was developed by Koutrakis and co-workers (12) and commercially available through Ogawa USA (Pompano Beach, FL). Initial study design had called for deployment of the passive O3 sampling device as a stand-alone sampler, but the passive O3 sampler’s performance was found to be unacceptable during validation studies performed prior to field operations. Inconsistent sampler response, due to apparent changes in effective sampling face velocity, was identified as a likely source of sampling bias (12-14). A controlled flow sampler, which permitted timed exposure VOL. 32, NO. 4, 1998 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Schematic of TED sampler. diffusion (TED) sampling with the Ogawa sampler, was developed for use, based on an initial concept developed by Michaud and Quackenboss (15). The sampler is schematically depicted in Figure 1. Sampler air flow was provided by a conventional cooling fan, and set to approximately 1.3 m/min, which had been previously determined to be in the optimal operational flow regime for Ogawa sampler operation (12). In a series of laboratory and field experiments, comparison of ambient O3 data collected by the TED sampler and commercial ultraviolet photometers revealed that the TED sampler was free of the sampling biases associated with the passive use of the Ogawa sampler, but had a +6% bias and (13% precision in sampling for O3 (13). On the basis of performance information from these experiments, the TED sampler approach was selected for use in this study. Nitrite-coated filters were purchased from Ogawa USA (Pompano Beach, FL) for study use. In an ozone-free environmental chamber, filters were loaded into one end of double-sided Teflon filter holders, sealed in plastic bags, placed in press-fit airtight vials, and kept under dark and refrigerated conditions prior to field use. TED samplers were loaded with two single-ended Teflon filter holders at each study home prior to sampling, and sampling was manually initiated for a continuous 24 h sampling period. Following sampling, filter holders were removed from the TED samplers, sealed in plastic bags, and placed into press-fit airtight vials. Exposed samples were returned to the laboratory as quickly as possible, but were typically kept sealed and refrigerated for several days during transfer from the field to the laboratory. Samples were refrigerated until ion chromatographic analyses for nitrate determination could be performed, typically within 1 week. For quality control purposes, continuous ultraviolet photometers (Dasibi Model 1003-AH, Glendale CA) were also 464
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deployed in and around five homes. In these study homes, photometers were operated over the same 24 h sampling period as the TED samplers, with the photometer sampling inlet within a meter of the TED sampling unit. Photometercollected data were used to calculate a 24 h integrated O3 value for comparison with the TED filter samples. Regional air monitoring data for the sampling periods of interest were obtained from the closest reporting community monitoring stations, typically located within a few kilometers of study homes. At the stations, ultraviolet photometers were used in conformance with prescribed regulatory air monitoring protocols and procedures. Continuous O3 data provided from the fixed-site sampling stations were combined to obtain cumulative time-based samples matched to those 24 h samples collected in the study homes. Walk-through surveys were performed in each home prior to sampling, and questionnaire interviews were completed by residents following each 24 h sampling period, to document housing factors of potential importance. These surveys included questions utilized by previous investigators (16, 17) to characterize home operation for a number of housing factors, including the use of heating and air conditioning, the presence and extent of carpeting in the home, the amount of time windows were left open during sampling, and the presence of smokers in the home.
Results A total of 481 residential TED measurements (241 indoor and 240 outdoor) were collected. These results are summarized in Table 1. Indoor O3 measurements were almost always below simultaneously measured outdoor levels, with three exceptions (two of which resulted from the comparison
TABLE 1. Summary of Ozone Data Using TED Samplers, for (a) Indoor, (b) Outdoor, and (c) Ratio Dataa (a) indoor O3 data
(b) outdoor O3 data
(c) indoor/outdoor ratio data
variable
indoor O3, ppb
variable
outdoor O3, ppb
variable
I/O ratio
mean, 24 h avg std dev min max no. of samples
13 12
