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Volume 105, Number 2, February 1997  

Research Article 

 

 

 

 

 

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Air Pollution and Emergency Room Visits for Asthma in Santa Clara
County, California 

Michael Lipsett,1 Susan Hurley,2 and Bart Ostro1 

1California Office of Environmental Health Hazard Assessment, Berkeley,
California 94704 USA; 2California Department of Health Services,
Berkeley, California 94704 USA 

  HYPERLINK "http://www.ehponline.org/members/1997/105-2/lipsett.html"
\l "abstract#abstract"  Abstract  

  HYPERLINK "http://www.ehponline.org/members/1997/105-2/lipsett.html"
\l "intro#intro"  Introduction  

  HYPERLINK "http://www.ehponline.org/members/1997/105-2/lipsett.html"
\l "data#data"  Data and Methods  

  HYPERLINK "http://www.ehponline.org/members/1997/105-2/lipsett.html"
\l "results#results"  Results  

  HYPERLINK "http://www.ehponline.org/members/1997/105-2/lipsett.html"
\l "discussion#discussion"  Discussion  

Abstract

 g/m3 change in PM10 (2-day lag) corresponded to RRs 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 significantly associated with any
pollutant variable. Several sensitivity analyses, 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 PM10 and
exacerbations of asthma in an area where one of the principal sources of
PM10 is RWC. Key words:   HYPERLINK
"http://www.ehponline.org/tagSearch/asthma"  asthma ,   HYPERLINK
"http://www.ehponline.org/tagSearch/emergency+room+visits"  emergency
room visits ,   HYPERLINK
"http://www.ehponline.org/tagSearch/epidemiology"  epidemiology ,  
HYPERLINK "http://www.ehponline.org/tagSearch/particulate+air+pollution"
 particulate air pollution ,   HYPERLINK
"http://www.ehponline.org/tagSearch/wood+smoke"  wood smoke . Environ
Health Perspect 105:216-222 (1997) 

Address correspondence to M. Lipsett, 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 Lianne Sheppard,
University 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
Protection 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

 m in aerodynamic diameter) in Santa Clara County, located at the
southern end of the Bay Area (2). During the source apportionment
sampling, RWC was the largest single identified source of winter PM10 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 molecular
weight aldehydes and acids, nitrogen oxides, and sulfur dioxide (3).
Indoor exposures to this complex mixture have previously been linked
with increased risks of respiratory infection and otitis, increased
symptoms of respiratory irritation, and exacerbations of asthma symptoms
(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 by 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 analysis, since
preliminary analyses had indicated the presence of significant
interhospital heterogeneity, possibly due to differences in diagnostic
preferences and in the populations served (one facility was a county
hospital, whereas the other two were private). Air monitoring data were
obtained from the BAAQMD for the principal San Jose monitoring site,
centrally located in the Santa Clara Valley. Particulate matter (PM)
metrics included coefficient of haze (COH), a measure of light
transmittance, which was recorded every 2 hr, and PM10, 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, PM10 was
measured only every sixth day. Ozone (O3) and nitrogen dioxide (NO2)
were measured continuously and reported by the BAAQMD as hourly
averages. We regressed measured PM10 on corresponding daily average
values of COH in order to predict missing PM10 values so that we could
conduct analyses with a daily PM10 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 analysis using
PC-SAS (SAS Institute, Cary, NC), S-Plus (StatSci, Seattle, WA), and
Stata (Stata Corp, College Station, TX) (12-14). 

The principal analysis 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-Poisson 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
standard errors are estimated by multiplying those obtained from the
Poisson model by a dispersion parameter. 

Initially we ran cross-correlations of the various explanatory variables
to examine whether multicollinearity would be a concern in subsequent
analyses. Pollutant variables found to be strongly 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, covariates 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 PM10 (described above). Gaseous
pollutants were modeled as daily peak 1-hr concentrations of O3 and NO2.
The influence of temperature was examined by using daily minimum
temperatures (including several lagged specifications) and by running
models with locally weighted regression (loess) smooths of temperature
(spans of 90 and 45 days) (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 days 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 analysis indicated the presence of significant
interhospital heterogeneity. Separate sets of indicator 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 document a variety of lag structures relating ambient
particle concentrations to both morbidity and daily mortality. Schwartz
and colleagues recently reported that the strongest associations of PM10
and asthma ER visits in Seattle were found when the explanatory variable
was defined as the mean of the previous 4 days' concentrations of PM10
(11). We investigated the effects of up to 5-day lags as well as
multiday averages of PM10. 

  = 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,
theoretically yielding robust estimators and correcting for serial
correlation 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. Accordingly, 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
successfully to reduce residual serial correlation in time-series data
similar to these. When an explanatory variable, 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, thereby
helping to reduce the degree of residual serial correlation. This
smoothing technique also allows for 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 modeling tool. Therefore, to control for temporal trends
in the data and to allow for potential nonlinearities in the effect of
temperature, we repeated the analysis using generalized additive models
with loess smooths of time and temperature. Because our data represent
discontinuous time series across years, we adjusted the span of the
smooth to both 90 days (the length of one winter) and 45 days (half of
the winter span). 

We undertook several additional sensitivity analyses, which included 1)
conducting robust regressions (to minimize the effects of outliers and
other potentially influential data points) using an iteratively
reweighted least squares methodology; 2) incorporating several
combinations of trigonometric terms to model and thereby reduce the
impact of any 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

  

  

 g/m3, 24-hr average). Using COH readings to predict PM10 on days when
it was not measured, however, resulted in estimates of 3 and 202
exceedances of the federal and state standards, respectively. O3
concentrations were generally at or near background levels throughout
most of the observation period, as expected, since ground-level O3 is
generated photochemically and attains elevated concentrations primarily
from April to October in California. NO2 concentrations were
substantially below the California 1-hr ambient air quality standard
(0.25 ppm) during the entire study. On most days there was little or no
precipitation. 

Table 2 is a correlation matrix of pollutant and meteorological
variables. Minimum temperature was negatively associated with PM10, COH,
and NO2, which would be expected to occur in the presence of the shallow
thermal inversions common during Northern California winters. NO2 was
strongly correlated with the particulate measures and thus was not used
in any initial regressions with PM10 as an explanatory variable. 

 g/m3). The 3-day average PM10 concentration attained statistical
significance (RR = 1.73, 95% CI = 1.00-2.97, p = 0.048); all other PM10
regression coefficients were of borderline significance (p = 0.06). 

 

Figure 1. The joint influence of PM10 and minimum temperature on asthma
emergency 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 PM10 and minimum
temperature on asthma ER visits. This graph suggests an interactive
effect of PM10 and temperature primarily on days when the minimum
temperature was less than the mean. Inclusion of an interaction term (PM
X minimum temperature) increased the magnitude of the PM10 regression
coefficients and resulted in statistical significance for all
specifications of PM10, as displayed in Table 3. 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 PM10 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 PM10 and the
interaction term (19). 

  

The formulas for calculating RR given the interaction between PM10 and
temperature took the following form: 

  

The results of these analyses are displayed in Table 3. As expected, the
estimated RRs were strongly 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 PM10). At 30°F, the RRs for most of the
specifications of PM10 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 PM10 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
PM10-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 specification of PM10. 

  

 g/m3 change in PM10 (at 20°F), using only 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 data 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 ranged 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 PM10; in general, models with PM10 fit the data slightly
better (data not shown). Using similar models with contemporaneous and
lagged exposures, asthma ER visits were not significantly associated
with O3 (e.g., for same-day ozone, using the full model, ßO3 = -0.003
[p>0.29]). However, same-day NO2 was associated with ER visits for
asthma (ßNO2 = 0.013 [p = 0.024]). When included as an explanatory
variable in regressions along with PM10, the NO2 coefficient became
insignificant, while that for PM10 did not change in magnitude and
remained statistically significant. In the models that included NO2 or
O3 rather than PM10 as the pollutant variable, the temperature
coefficient became highly significant (p<0.01). In models using
unlagged, lagged, and multiday averages of pollutant variables,
gastroenteritis ER visits were not significantly associated with PM10,
COH, NO2 or O3 (data not shown). 

Discussion

In this investigation we found that a variety of specifications of PM10
were consistently associated with ER visits for asthma, but not for
gastroenteritis. Several lagged specifications for PM10 provided
modestly stronger associations with asthma ER visits than did same-day
PM10. 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 generally tend to
increase markedly 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 severe symptomatic reactions to exposure
(manifested by a visit to the ER) were delayed by 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 penetration 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 emitted from wood-burning devices in
their own homes. A recent report indicates that use of a fireplace or
wood stove on a given day strongly predicts exacerbation of respiratory
symptoms 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 analysis 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 days, 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 PM10 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 clearly 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 analysis 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 PM10 X
minimum temperature interaction term increased both the magnitude and
statistical significance of the PM10 coefficient, while the opposite was
true for the coefficient for minimum temperature. In most model
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 adequately in other models. However, other specifications,
including loess smooths of temperature, did not significantly improve
the model fit without the interaction term. 

 -0.48 for the full PM10 data set, including the days predicted from
COH). During Bay Area winters, radiative inversions associated with low
temperature frequently limit the vertical mixing 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 asthma ER visits with higher PM10
concentrations in conjunction with colder days 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
probability 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 individual
exposures, which may confound or modify the relationships observed
between ambient PM10 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 symptoms of asthma in other settings, but in those
situations the concentrations were substantially higher than here
(23-25). The strong correlation of NO2 with PM10 and COH probably
reflects in part the contribution of motor vehicle emissions to winter
particle loading. Nevertheless, NO2 was associated with the outcome only
for same-day exposures. Although NO2 is a strong respiratory irritant,
chamber studies of asthmatics suggest that the NO2 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 NO2 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
NO2 and asthma ER visits in this data set, in addition to the
observation that the NO2 regression coefficient lost its statistical
significance in models that also included PM10, suggest that the
same-day association may be an artifact of covariation with PM10. 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
PM10 than NO2 with asthma ER visits may lie in the greater likelihood of
exposure misclassification for the latter pollutant. High-temperature
combustion results in the formation of NO2 and other nitrogen oxides,
which tend to be elevated near streets and freeways with substantial
traffic volume. Thus, a single NO2 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 hourly NO2 concentrations at three regional
fixed-site monitors, including the one used in this analysis, 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 PM10 at the same sites ranged from 0.90 to
0.92. Thus, even if NO2 exposure was causally related to serious asthma
exacerbations, these results suggest that exposure misclassification
alone could have resulted in an apparently stronger relationship of ER
asthma visits with PM10 than with NO2, since such misclassification
tends to bias the results towards the null hypothesis of no effect. 

Sulfur dioxide (SO2), another respiratory irritant to which asthmatics
tend to be susceptible, was not measured by the BAAQMD during the study
period because SO2 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 SO2 was measured at this site, the
peak 1-hr concentration was 4 ppb, whereas the annual average was 0.55
ppb. [For purposes of comparison, the California ambient air quality
standard for SO2, which is intended to protect individuals with asthma,
is 250 ppb (1-hr average)]. With such low SO2 concentrations, the
sulfate fraction of PM10 also tends to be quite low compared with other
regions in the United States. During the winters of 1988-1991, sulfates
composed only about 4-5% of PM10 mass measured at the same monitoring
site as that used in this analysis (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 PM10, it is possible that the increased RRs reported here
may have been confounded by 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
PM10-associated 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 PM10 and
asthma exacerbations, it would be inappropriate to control for this
variable in an analysis of potential air pollution effects (32). 

 g/m3 change in PM10 (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
PM10-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
PM10 concentrations was observed primarily at lower temperatures, it is
possible that this association would not hold during other seasons.
Moreover, though RWC takes place throughout the year in the Bay Area,
overall PM10 levels and absolute concentrations of RWC-associated
particles are generally 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 currently examining the
relationships of PM10 and other ambient pollutants to respiratory and
cardiovascular morbidity in the Bay Area throughout the year. 

Our results are consistent with numerous other recent reports linking
airborne particles to adverse respiratory outcomes when measured outdoor
concentrations are lower than the federal ambient air quality standard
for PM10 (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 PM10 (or fine particles, which
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 respiratory irritants in
wood smoke, including formaldehyde, acrolein, acetaldehyde, 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 narrow inland valleys in Santa Clara
County the percentage is likely to have been substantially higher. Thus,
though PM10 is a routinely monitored indicator for wood smoke, the
respiratory toxicity of the mixture, rather than particles per se, may
have driven 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 exclusively to the latter. 

Unlike most other human activities that generate air pollution, RWC
remains largely unregulated, despite substantial contributions to
atmospheric particle loading in many areas of North America, especially
in the western United States. It is also a major source of indoor
pollution in many developing countries. Though wood combustion is
probably the oldest form of anthropogenic pollution, there is a paucity
of data on health impacts of smoke inhalation except at high
concentrations. Future research should address effects of ambient levels
of wood smoke on sensitive subpopulations (i.e., asthmatics) in chamber
studies and in epidemiologic investigations in areas where RWC is the
primary source of air pollution. 

References 

1. BAAQMD. Workshop Notice, Regulation 12, Rule 9: Residential Wood
Combustion, Bay Area Air Quality Management District. San Francisco,
CA:Bay Area Air Quality Management District, 1994. 

2. Chow JC, Fairley D, Watson JG, De Mandel R, Fujita EM, Lowenthal DH,
Lu Z, Frazier CA, Long G, Cordova J. Source apportionment of wintertime
PM10 at San Jose, California. J Environ Eng 121:378-387 (1995). 

3. Larson TV, Koenig JQ. Wood smoke: emissions and noncancer respiratory
effects. Annu Rev Public Health 15:133-156 (1994). 

4. Honicky RE, Akpom CA, Osborne JS. Infant respiratory illness and
indoor air pollution from a wood-burning stove. Pediatrics 71:126-128
(1983). 

5. Honicky RE, Osborne JS, Akpom CA. Symptoms of respiratory illness in
young children and the use of wood-burning stoves for indoor heating.
Pediatrics 75:587-593 (1985). 

6. Morris K, Morganlander M, Coulehan JL, Gahagen S, Arena VC.
Wood-burning stoves and lower respiratory tract infection in American
Indian children. Am J Dis Child 144:105-108 (1990). 

7. Daigler GE, Marked SDI, Cummins KM. The effect of indoor air
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