Environmental Health Perspectives Volume 105, Number 2, February 1997 Research A rticle

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### Ai r Pollution and Emergency Room Visits for A sthma in Santa Clara County, California Michael Lipsett, 1 Susan Hurley, 2 and Bart Ostro 1 1 California Office of Environmental Health Hazard Assessment, Berkeley , California 94704 USA; 2 California Department of Health Services, Berkeley , California 94704 USA

 Ab stract  Introduction  Data and Methods  Results  Discussion Abstract During the winters of 1986 - 1987 through 1991 - 1992, rainfall throughout much of Northern California was subnormal, resulting in intermittent accumulation of air pollution, much of which was attributable to residential wood combusti on (RWC) . This investigation examined whether there was a relationship between ambient air pollution in Santa Clara County , California and emergency room visits for asthma during the winters of 1988 - 1989 through 1991 - 1992. Emergency room (ER) records from three acute - care hospitals were abstracted to compile daily visits for asthma and a control diagnosis (gastroenteritis) for 3 - month periods during each winter. Air monitoring data included dail y coefficient of haze (COH) and every - other - day particulate matter with aerodynamic diameter equal to or less than 10 microns (PM 10 , 24 - hr average) , as well as hourly nitrogen dioxide and ozone concentrations. Daily

COH measurements were used to predict values for missing day s of PM 10 to develop a complete PM 10 time series. Daily data were also obtained for temperature, precipitation, and relative humidity . In time - series anal ys es using Poisson regression, consistent relationships were found between ER visits for asthma and PM 10 . Same - day nitrogen dioxide concentrati ons were also associated with asthma ER visits, while ozone was not. Because there was a significant interaction between PM 10 and minimum temperature in this data set, estimates of relative risks (RRs) for PM 10 - associated asthma ER visits were temperature - dependent. A 60 g/m 3 change in PM 10 (2 - day lag) corresponded toRRs of 1.43 (95% CI= 1.18 - 1.69) at 20°F, representing the low end of the temperature

distribution, 1.27 (95% CI = 1.13 - 1.42) at 30°F, and 1.11 (95% CI = 1.03 - 1.19) at 41°F, the mean of the observed minimum temperatures. ER visits for gastroenteritis were not significantl y associated with any pollutant variable. Several sensitivity analy ses, including the use of robust regressions and of nonparametric methods for fitting time trends and temperature effects in the data, supported these findings. These results demonstrate an association between ambient wintertime PM 10 and exacerbations of asthma in an area where one of the principal sources of PM 10 is RWC. Key words: asthma , emergency room visits , epidemiology , particulate air pollution , wo od smoke . Environ Health Perspect 105:216 - 222 (1997) Address correspondence to M. L ipsett, California Office of Environmental Health Hazard Assessment, California Environmental Protection Agency , 2151 Berkeley Way , Annex 11, Berkeley , CA 94704 USA. The authors would like to acknowledge the assistance of Constance Heye in abstracting the emergency

room data, the staff of the Bay Area Air Quality

Management District for providing the air monitoring data, David Fairley , Bay Area Air Quality Management District, and L ianne Sheppard, Universit y of Washington, for helpful comments on earlier drafts. The contents and opinions expressed in this manuscript are those of the authors and do not represent the official position of the Office of Environmental Health Hazard Assessment, the California Environmental Protec tion Agency , or the State of California. This paper was presented in part at an International Specialty Conference on Particulate Matter, Pittsburgh, PA, sponsored by the Air & Waste Management Association, April 1995, and at the annual meeting of the American Thoracic Society , Seattle WA, May 1995. Received 11 June 1996 ; accepted 13 November 1996. 

### Introduction Beginning with the winter of 1986 - 1987, Northern California experienced a 6 - ye ar drought during which man y inland valleys were subject to intermittent air stagnation and decreased visibility . During this period, there were numerous anecdotal reports that smoke from residential wood combustion (RWC) was causing adverse respiratory effects. There are an estimated 1 - 1.5 million fireplaces and 300,000 wood - burning stoves in the San Francisco Bay Area (1). Source apportionment analysis sponsored b y the Bay Area Air Quality Management District (BAAQMD) has shown that, on average, RWC accounts for approximately

45% of winter PM 10 (particulate matte r less than or equal to 10 m in aerodynamic diameter) in Santa Clara County, located at the southern end of the Bay Area (2). During the source apportionment sampling, RWC wa s the largest single identified source of winter PM 10 in Santa Clara County, quantitatively approximating the sum of motor vehicle emissions and entrained road dust. Wood smoke contains many respiratory irritants in addition to particles, including low mol ecular weight aldeh ydes and acids, nitrogen oxides, and sulfur dioxide (3). Indoor exposures to this complex mixture havepreviously been linked with increased risks of respiratory infection and otitis, increased sy mptoms of respiratory

irritation, and exa cerbations of asthma sy mptoms (4 - 8). Several studies undertaken in the Pacific Northwest suggest that ambient particles, many of which are attributable to RWC, are linked with decrements in children's lung function and increased hospital emergency room (ER) visits for asthma (9 - 11). This investigation was initiated to examine the relationship between ER visits for asthma and ambient air pollutant concentrations in Santa Clara County during the winters of 1988 - 1989 through 1991 - 1992. 

### Data and Methods Visits for asthma to three acute - care facilities in Santa Clara County for the winters of 1988 - 1989 through 1991 - 1992 (1 November - 31 January) were abstracted from ER logbooks b y one of the co - authors (Hurley) and an epidemiology graduate student. Visits for gastroenteritis, a control diagnosis considered unlikely to be related to air pollution, were also abstracted. Daily counts of ER visits for asthma and gastroenteritis were compiled for each hospital. Summed hospital - specific counts were used as the primary dependent variable in the anal ys is, since preliminary anal ys es had indicated the presence of significant interhospital heterogeneity , possibl y due to differences in diagnostic preferences and in the populations served (one facility was a county hospital, whe reas the other two were private). Air monitoring data were obtained from the BAAQMD for the principal San Jose monitoring site, centrall y located in the Santa Clara Valley. Particulate matter (PM) metrics included coefficient of haze (COH), a measure of li ght transmittance, which was recorded every 2 hr, and PM 10 , which was recorded as a 24 - hr average with a high - volume sampler every other day . For one 45 - day period during the 1991 - 1992 winter, however, PM 10 was measured only every sixth day . Ozone (O 3 ) and nitrogen dioxide (NO 2 ) were measured continuously and reported by

the BAAQMD as hourly averages. We regressed measured PM 10 on corresponding daily average values of COH in order to predict missing PM 10 values so that we could conduct anal ys es with a daily PM 10 metric. The R2 value for this linear predictive model for the winters of 1988 - 1989 through 1991 - 1992 was 0.81. Meteor - ological data included daily temperature, relative humidity , and precipitation, which were obtained from the National Climatic Data Center in Asheville, North Carolina. The meteorological, air quality , and health data were entered and merged for anal ys is using PC - SAS (SAS Institute, Cary , NC), S - Plus (StatSci, Seattle, WA), and Stata (Stata Corp, College Station, TX) (12 - 14). The prin cipal anal ys is relied on Poisson regression, which assumes that counts of independent, rare events follow a Poisson distribution, conditional on the explanatory variables. However, heterogeneity among the asthmatics' exposures, respiratory infections, and other factors may

result in overdispersion of the data in relation to a classic Poisson distribution. This in turn may affect the standard errors of the regression coefficients, leading to incorrect significance tests. To address this issue, the extra - Pois son variability was modeled and incorporated into the estimates of the standard errors using PROC GENMOD in PC - SAS, which adopts the approach of McCullagh and Nelder (15). While the estimated coefficients remain the same as in Poisson regression, the stand ard errors are estimated by

multiplying those obtained from the Poisson model by a dispersionparameter.

Initiall y we ran cross - correlations of the various explanatory variables to examine whether multicollinearity

would be a concern in subsequent analy se s. Pollutant variables found to be strongl y correlated (r> 0.6) were not included together in the regression models. We then ran univariate regressions of asthma ER visits on each of the explanatory meteorological and pollutant variables. In general, covari ates were examined in multivariate models if their t - statistics in the univariate regressions were equal to or greater than one. The PM variables were examined as daily average COH and PM 10 (described above). Gaseous pollutants were modeled as daily peak 1 - hr concentrations of O 3 and NO 2 . The influence of temperature was examined by using dail y minimum temperatures (including several lagged specifications) and b y running models with locally weighted regression (loess) smooths of temperature (spans of 90 and 45 day s) (see below). Percent relative humidity (measured daily at 4 p.m.) was examined as a continuous variable, with contemporaneous and lagged values of up to 4 previous day s considered. Precipitation was modeled as a binary variable. To control for interhospital differences in ER utilization, the regression models also included indicator variables representing each hospital since, as noted above, preliminary anal ys is indicated the presence of significant interhospital heterogeneit y. S eparate sets of in dicator variables were also included to model short - term (i.e., day of week) and long term (i.e., annual) trends in ER utilization. Several terms were used to model potential interactions between temperature and the pollutant variables. Numerous reports d ocument a variety of lag structures relating ambient particle concentrations to both morbidity and dail y mortality . Schwartz and colleagues recently reported that the strongest associations of PM 10 and asthma ER visits in Seattle were found when the explan atory

variable was defined as the mean of the previous 4 day s' concentrations of PM 10 (11). We investigated the effects of up to 5 - day lags as well as multiday averages of PM 10 . Positive serial correlation is a common feature of time - series data, and can result in underestimation of the standard errors of regression coefficients. Examination of autocorrelation function (ACF) plots of the model residuals showed slight serial correlation only at lag 1 ( = 0.12 - 0.14 for the various models). This minimal degree of serial correlation is unlikely

to produce biased significance tests (16). Nevertheless, to address this issue, we reran the models using the general estimating equations (GEE) of Liang and Zeger (17). In this approach, the covariance structure is incorporated into the estimation of the regression coefficients in addition to their variances, theoreticall y yi elding robust estimators and correcting for serial correl ation in the data. Though this method has been used in other time - series data sets (11), recent simulations suggest that when the data are structured in few independent blocks, the GEE model may overstate the significance of regression coefficients. Accordingl y, when using the GEE, we structured the data into 12 blocks (by

hospital and year). Unlike Burnett and colleagues, we did not structure these blocks as random effects variables (18). Models that incorporate loess smooths of time also have been used s uccessfully to reduce residual serial correlation in time - series data similar to these. When an explanatoryvariable, smoothed in this fashion, is incorporated into a regression, the measured value of

the variable is replaced with a locally weighted moving average and the regression is conducted on the moving average. Thus, adding a loess smooth of time diminishes short term fluctuations in the data, thereb y helping to reduce the degree of residual serial correlation. This smoothing technique also allows fo r more parsimonious modeling of annual temporal trends in the data than the use of indicator variables in standard Poisson regressions. Furthermore, loess smoothing techniques can accommodate nonlinear patterns, offering a more flexible nonparametric model ing tool. Therefore, to control for temporal trends in the data and to allow for potential nonlinearities in the effect of temperature, we repeated the anal ys is using generalized additive models with loess smooths of time and temperature. Because our data represent discontinuous time series across ye ars, we adjusted the span of the smooth to both 90 day s (the length of one winter) and 45 day s (half of the winter span). We undertook several additional sensitivity anal ys es, which included 1) conducting robus t regressions (to minimize the effects of outliers and other potentially influential data points) using an iterativel y reweighted least squares methodology; 2) incorporating several combinations of trigonometric terms to model and thereb y reduce the impact of an y long wave trends within the winter seasons that were not obvious by visual inspection of the data; and 3) fitting the same models to ER visits for gastroenteritis (the control diagnosis) as those used for asthma. 

### Results

Descriptive statistics for the variables used in this investigation are displayed in Table 1. The mean PM 10 concentration during the study period was 61.2 g/m 3 . During the 368 observation - day s of this investigation, there were 2 measured exceedances of the federal ambient air quality

standard for PM 10 (150 g/m 3 , 24 - hr average), and 85 of the Californiastandard (50 g/m 3 , 24 - hr average). Using COH readings to predict PM 10 on day s when it

was not measured, however, resulted in estimates of 3 and 202 exceedances of the federal and state standards, respectively . O 3 concentrations were generally at or near background levels throughout most of the observation period, as expected, since ground - level O 3 is generated photochemically and attains elevated concentrations primaril y from April to October in California. NO 2 concentrations were substantially below the California 1 - hr ambient air quality

standard (0.25 ppm) during the entire study . On most day s there was little or no precipitation. Table 2 is a correlation matrix of pollutant and meteorological variables. Minimum temperature was negatively associated with PM 10 , COH, and NO 2 , which would be expected to occur in the presence of the shallow thermal inversions common during Northern California winters. NO 2 was strongly correlated with the particulate measures and thus was not used in an y initial regressions with PM 10 as an explanatory variable. In this paper, a relative risk (RR) reported for a pollutant variable represents the change in daily risk of seeking ER treatment for asthma associated with a given unit change in the ambient level of that pollutant. For PM 10 , we calculated RRs associated with a 60 - unit change. Specificall y , the RRs we report for PM 10 represent ratios of the expected counts of asthma ER visits on day s when 24 - hr average ambient levels of PM 10 were 60 g/m 3 higher than on compari son day s. While this is a moderatel y large pollution difference, it is well within the range of PM 10 observed and is approximately equal to the mean PM 10 levels for the study period. The initial estimates of the PM 10 - associated RRs for asthma ER visits wer e based on regressions that did not include a term for interaction between PM 10 and minimum temperature. These RRs ranged from 1.52 to 1.79, depending on the PM 10 metric used (i.e., unlagged and lagged pollutant variables as well as multiday averages), whe n calculated at the approximate mean concentration of PM 10 (60 g/m 3 ). The 3 - day average PM 10 concentration attained statistical significance (RR = 1.73, 95% CI= 1.00 - 2.97, p = 0.048); all other PM 10 regression coefficients were of borderline significance (p = 0.06). Figure 1. The joint influence of PM 10 and minimum temperature on asthma e mergency room visits, Santa Clara County , California, in the winters of 1988 - 1989 through 1991 - 1992. Figure 1 is a plot of the joint influence of PM 10 and minimum temperature on asthma ER visits. This graph suggests an interactive effect of PM 10 and temp erature primarily on day s when the minimum temperature was less than the mean. Inclusion of an interaction term (PM X minimum temperature) increased the magnitude of the PM 10 regression coefficients and resulted in statistical significance for all specific ations of PM 10 , as displayed in Table3. The coefficient for the interaction term included in the regression models, though of

small magnitude, was significant (p<0.05 in all but one model). In these models, the RR estimates for PM 10 depend on the value of the interacting variable (i.e., minimum temperature). Therefore, we estimated RRs and confidence intervals using formulas that incorporated specific values for minimum daily temperature and the covariance of PM 10 and the interaction term (19).

The formulas for calculating RR given the interaction between PM 10 and temperature took the following form:

The results of these anal yses are displayed in Table 3. As expected, the estimated RRs were strongl y temperature - dependent. At the low end of the temperature distribution (20°F), the RRs ranged from 1.33 (3 - day average) to 1.66 (1 - day

lag of PM 10 ). At 30°F, the RRs for most of the speci fications of PM 10 were statistically significant, though the magnitudes of the RRs were less than those calculated at the lower temperature. At 41°F, the mean minimum daily temperature for the study period, only

the 2 - day lag of PM 10 remained significant ( RR = 1.11, 95% CI = 1.03 - 1.19). The GEE models produced somewhat lower estimates of the RRs, and none calculated at 41°F were significant. In contrast, the robust regressions resulted in somewhat higher estimates of PM 10 - associated RRs at 20°F and 30°F, but not at 41°F. Specification of a variety of trigonometric terms in the regression equations did not substantially affect the results. Table 4 compares the results from the Poisson, robust, GEE, and trigonometric model regressions for a 2 - day lagged specif

ication of PM 10 .Models run with loess smooths of temperature and t ime (with spans of 90 or 45 day s) did

not markedly

change the magnitude of the PM 10 coefficients, though in several instances their significance decreased slightly . However, in these instances, coefficients for all various specifications of PM 10 remained s ignificant at p < 0.05, except the 4 - day lag. When the regressions were analyzed with temperature stratified into two levels (at the mean), the PM 10 coefficients remained significant only for temperatures < 41°F. Point estimates for RRs corresponding to a 60 g/m 3 change in PM 10 (at 20°F), using onl y the days when T<41°F, ranged from 1.41 (4 - day lag) to 2.17 (no lag), somewhat higher than the estimates derived using the entire dat a set. Insufficient statistical power precluded additional stratification on temperature. The addition of loess smooths of temperature and time to the regression models did not eliminate the small degree of autocorrelation at lag one. Other terms that were consistently associated with the outcome variable included the indicators for precipitation, hospital, day of week, day

of study , and year. Relative humidity was not significantly

related to ER visits for asthma in any of the initial models (p - values ran ged from 0.5 - 0.7) and therefore was dropped from all subsequent runs. The models using COH as the particulate metric were not markedly different from those using PM 10 ; in general, models with PM 10 fit the data slightl y better (data not shown). Using similar models with contemporaneous and lagged exposures, asthma ER visits were not significantl y associated with O 3 (e.g., for same - day ozone, using the full model, ß O3 = - 0.003 [p>0.29]). However, same - day NO 2 was associated with ER visits for asthma (ß NO2 = 0 .013 [p = 0.024]). When included as an explanatory variable in regressions along with PM 10 , the NO 2 coefficient became insignificant, while that for PM 10 did not change in magnitude and remained statistically significant. In the models that included NO 2 or O 3 rather than PM 10 as the pollutant variable, the temperature coefficient became highl y significant (p<0.01). In models using unlagged, lagged, and multiday averages of pollutant variables, gastroenteritis ER visits were not significantly associated with PM 10 , COH, NO 2 or O 3 (data not shown). 

### Discussion In this investigation we found that a variety of specifications of PM 10 were consistently associated with ER visits for asthma, but not for gastroenteritis. Several lagged specifications for PM 10 provided modestly stronger associations with asthma ER visits than did same - day

PM 10 . One explanation for this observation may lie in the pattern of exposure to winter particles in the Santa Clara Valley . Visual inspection of the 2 - hr COH values and the results of more recent real - time monitoring data indicate that PM levels generall y tend to increase markedl y in the late afternoon and evening, a pattern that is consistent with RWC emissions. Peak levels, therefore, often occur in the evening, suggesting that if se vere s ymptomatic reactions to exposure (manifested by a visit to the ER) were delayedby

more than a few hours, they would not be observed until the following day or later. In Santa Clara County, the main source of winter particle concentrations is RWC, though motor vehicle exhaust and entrained road dust also make significant contributions (2). Wood smoke particles arise mainly from condensation of combustion gases and therefore tend to be distributed mainly in the submicron range, allowing substantial pe netration to the indoor environment from outdoors (3). Wood smoke can also enter a residence directly via backdrafting from a fireplace or wood stove: It is possible that some of the asthmatics who later sought care at the local ER were exposed to smoke em itted from wood - burning devices in their own homes. A recent report indicates that use of a fireplace or wood stove on a given day

strongl y predicts exacerbation of respiratory s ymptoms in adults who have moderate or severe asthma (8). However, because of the ecological design of this ER visit study , we could not assess individual exposures. Others have also found delayed asthmatic responses to particulate or smoke exposure (20,21). In an anal ys is of ER visits after the Berkeley- Oakland hills firestorm of 1991, Shusterman and colleagues found that the mean lag between exposure to smoke and a visit to the ER was between 1 and 2 day s, which is also consistent with our findings [D. Shusterman, personal communication; (21)]. In Seattle, an area that has also experienced substantial wood smoke pollution, Schwartz and co - workers found that the mean PM 10 concentration averaged over a 4 - day period was the best predictor of ER asthma visits, also suggesting a delayed response (11). In a study of daily mortality in Santa Clara County in relation to particulate air pollution, Fairley found that the strongest association with the outcome was a 2 - day

lag (22). Although daily

mortality is clearl y not directly

comparable to asthma ER visits, finding delayed particle - associated adverse health events in the same locale lends additional plausibility to each report. Our anal ys is also controlled for meteorological factors, and suggested that the combination of low temperature and particle concentrations is also an important predictor of asthma ER visits. Inclusion of the PM 10 X minimum temperature interaction term increased both the magnitude and statistical significance of the PM 10 coefficient, while the opposite was true for the coefficient for minimum temperature. In most mode l specifications the interaction term itself, though of small magnitude, was significant (p<0.05) and its inclusion improved the overall model fit. The significance of the PM X minimum temperature term in these regressions may

be partly attributable to not having specified temperature adequatel y in other models. However, other specifications, including loess smooths of temperature, did not significantl y improve the model fit without the interaction term. Interpretation of this statistical interaction in me aningful biological terms is somewhat speculative. While exposure to cold dry air can precipitate bronchospasm in asthmatics, temperature was not strongl y correlated with asthma ER visits in models that also included PM 10 . However, in models where the pollutant variable was O 3 or NO 2 , temperature was strongl y associated with asthma ER visits. This difference may be attributable in part to the inverse correlation between PM 10 and temperature (r - 0.41 for measured PM 10 and r - 0.48 for the full PM 10 data set, including the days predicted from COH). During Bay Area winters, radiative inversions asso ciated with low temperature frequentl y limit the verticalmixing depth to less than 100 ft, trapping pollutants near ground level. Moreover, people

tend to use their fireplaces and wood stoves more often as the temperature drops. Thus, the association of a sthma ER visits with higher PM 10 concentrations in conjunction with colder day s and nights may

be partially

explicable by

both meteorology

and human behavior. Decreasing ambient temperatures tend to be associated with increasing time spent inside, thus enhancing the likelihood of exposure to indoor allergens and pollutants (including backdrafted wood smoke). Acute exposures to several indoor sources of combustion (gas stoves, cigarette smoke, and wood - burning devices) have been reported to enhance the prob ability

of an exacerbation of asthma in those with moderate to severe disease, which would include individuals likely to require intermittent urgent care (8). However, as noted earlier, the ecological design of this investigation precluded evaluation of in dividual exposures, which may

confound or modify

the relationships observed between ambient PM 10 levels and asthma ER visits. The results are not likely

to have been confounded by other measured pollutants or meteorological factors. Ozone has been associated with ER visits or increased sy mptoms of asthma in other settings, but in those situations the concentrations were substantially higher than here (23 - 25). The strong correlation of NO 2 with PM 10 and COH probabl y reflects in part the contribution of moto r vehicle emissions to winter particle loading. Nevertheless, NO 2 was associated with the outcome only for same - day

exposures. Although NO 2 is a strong respiratory irritant, chamber studies of asthmatics suggest that the NO 2 concentrations observed in this study would be unlikely

to elicit a bronchoconstrictive response (26,27). However, participants in chamber studies do not represent the spectrum of disease in the general population and the short (usually 1 - or 2 - hr) durations of the controlled studies cannot adequately capture the complexity

of real world exposures. Others have reported a relationship between ambient NO 2 and ER visits for asthma and with pulmonary function changes in asthmatics (28,29). Nevertheless, the absence of an association between lagged or multiday specifications of NO 2 and asthma ER visits in this data set, in addition to the observation that the NO 2 regression coefficient lost its statistical significance in models that also included PM 10 , suggest that the same - day association may be an artifact of covariation with PM 10 . This association, in turn, could also be due to the shallow thermal inversions in the Santa Clara Valley . Another possible explanation for the apparently stronger association of PM 10 than NO 2 with asthma ER visit s may lie in the greater likelihood of exposure misclassification for the latter pollutant. High - temperature combustion results in the formation of NO 2 and other nitrogen oxides, which tend to be elevated near streets and freeway s with substantial traffic volume. Thus, a single NO 2 monitoring site, as was used in this analysis, may not be representative of regional concentrations or personal exposures. In examining this issue, we ran pair - wise correlations among peak hourl y NO 2 concentrations at three regio nal fixed - site monitors, including the one used in this anal ys is, the coefficients of which ranged from 0.73 to 0.78 during the study period. Similar correlation coefficients (based on every- sixth - day

sampling) among 24 - hr averages for PM 10 at the same sit es ranged from 0.90 to 0.92. Thus, even if NO 2 exposure was causally related to serious asthma exacerbations, these results suggest that exposure misclassification alone could haveresulted in an apparentl y stronger relationship of ER asthma visits with PM 10 than with

NO 2 , since such misclassification tends to bias the results towards the null hypothesis of no effect. Sulfur dioxide (SO 2 ), another respiratory irritant to which asthmatics tend to be susceptible, was not measured b y the BAAQMD during the stu dy period because SO 2 concentrations in prior years had been far below both the California and the federal ambient air quality

standards. In 1988, for example, the last year for which SO 2 was measured at this site, the peak 1 - hr concentration was 4 ppb, wh ereas the annual average was 0.55 ppb. [For purposes of comparison, the California ambient air quality standard for SO 2 , which is intended to protect individuals with asthma, is 250 ppb (1 - hr average)]. With such low SO 2 concentrations, the sulfate fractio n of PM 10 also tends to be quite low compared with other regions in the United States. During the winters of 1988 - 1991, sulfates composed onl y about 4 - 5% of PM 10 mass measured at the same monitoring site as that used in this analy sis (30). In contrast, in urban areas on the East Coast, the comparable percentage is closer to 30% (31). Since some fungal spores and pollen fragments are within the size range encompassed by PM 10 , it is possible that the increased RRs reported here may have been confounded b y exposure to aeroallergens, some of which are well recognized to bear a causal relationship to seasonal and epidemic asthma exacerbations. While theoretically possible, such confounding is unlikely

to explain the associations observed here, where the PM 10 - ass ociated risk increased with decreasing temperature. Viral epidemics are also often cited as potential confounders of associations between air pollution and asthma or other respiratory

conditions. We did not have data available to control for this potential confounder. Moreover, to the extent that respiratory viral infections may

constitute an intermediate stage on a causal pathway between exposure to PM 10 and asthma exacerbations, it would be inappropriate to control for this variable in an analy sis of pote ntial air pollution effects (32). Our results indicate that a 10 g/m 3 increase in PM 10 (2 - day lag) would correspond to a relative risk of 1.06 (95% CI= 1.02 - 1.10) at 20°F, declining to 1.04 (95% CI= 1.02 - 1.06) at 30°F, and 1.02 (1.00 - 1.03) at the mean observed minimum temperature (41°F) using the Poisson model. These estimates were slightly lower using the GEE model (RR = 1.05; 95% CI= 1.02 - 1.08) and slightly higher using robust regressions (RR = 1.13; 95% CI= 1.03 - 1.24). In Seattle, where RWC has also been a major source of winter particles, Schwartz and co - workers reported a RR of 1.12 for a 30 g/m 3 increase in PM 10 for asthma ER visits, but their estimate was based on a multiday average of PM 10 concentrations throughout the ye ar (11). Using our data, a 30 g/m 3 change in PM 10 (lag 2) would result in a RR of 1.20 (95% CI = 1.07 - 1.33) at 20°F, a RR of 1.13 (95% CI = 1.06 - 1.20) at 30°F, or 1.06 (95% CI = 1.02 - 1.10) at 41°F. Though Schwartz et al. did not report any PM 10 - temperature interactions, their RR estimates are of comparable magnitude to ours. Whether these results could be replicated in seasons other than winter has not been examined. Given that the relationship between ER visits and PM 10 concentrations was observed primarily

at lower temperatures, it is po ssible that this association would not holdduring other seasons. Moreover, though RWC takes place throughout the year in the Bay

Area, overall PM 10 levels and absolute concentrations of RWC - associated particles are generall y lower in the spring, summer, and fall. On the other hand, during other seasons ambient levels of ozone and aeroallergens are more likely to be elevated, which could also increase the risk of ER visits for asthma. We are currentl y examining the relationships of PM 10 and other ambient po llutants to respiratory and cardiovascular morbidity in the Bay Area throughout the ye ar. Our results are consistent with numerous other recent reports linking airborne particles to adverse respiratory

outcomes when measured outdoor concentrations are low er than the federal ambient air quality standard for PM 10 (33). To the extent that the associations noted in this report represent causal relationships, it is plausible that the heterogeneous categories of substances subsumed by PM 10 (or fine particles, wh ich represent the bulk of RWC related particles and which also are capable of substantial penetration to the indoor environment) may

be responsible. In this study of winter air pollution and asthma, however, airborne particles represent but one of many res piratory

irritants in wood smoke, including formaldeh yde, acrolein, acetaldeh yde, acetic acid, phenol, and nitrogen oxides, among others (3). Though the mean contribution of wood smoke to particle mass was reported to be approximately 45%, in some of the n arrow inland valleys in Santa Clara County the percentage is likely

to have been substantially higher. Thus, though PM 10 is a routinely monitored indicator for wood smoke, the respiratory toxicity of the mixture, rather than particles per se, may have driv en the relationships reported here. On the other hand, since approximately half of the winter particle mass during this study period was attributable to sources other than RWC, one cannot directly link the elevated RRs observed here exclusivel y to the latt er. Unlike most other human activities that generate air pollution, RWC remains largel y unregulated, despite substantial contributions to atmospheric particle loading in many areas of North America, especially in the western United States. It is also a ma jor source of indoor pollution in many

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