Article | Published:

The use of alternative pollutant metrics in time-series studies of ambient air pollution and respiratory emergency department visits

Journal of Exposure Science and Environmental Epidemiology volume 21, pages 1019 (2011) | Download Citation

Abstract

Various temporal metrics of daily pollution levels have been used to examine the relationships between air pollutants and acute health outcomes. However, daily metrics of the same pollutant have rarely been systematically compared within a study. In this analysis, we describe the variability of effect estimates attributable to the use of different temporal metrics of daily pollution levels. We obtained hourly measurements of ambient particulate matter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3) from air monitoring networks in 20-county Atlanta for the time period 1993–2004. For each pollutant, we created (1) a daily 1-h maximum; (2) a 24-h average; (3) a commute average; (4) a daytime average; (5) a nighttime average; and (6) a daily 8-h maximum (only for O3). Using Poisson generalized linear models, we examined associations between daily counts of respiratory emergency department visits and the previous day's pollutant metrics. Variability was greatest across O3 metrics, with the 8-h maximum, 1-h maximum, and daytime metrics yielding strong positive associations and the nighttime O3 metric yielding a negative association (likely reflecting confounding by air pollutants oxidized by O3). With the exception of daytime metric, all of the CO and NO2 metrics were positively associated with respiratory emergency department visits. Differences in observed associations with respiratory emergency room visits among temporal metrics of the same pollutant were influenced by the diurnal patterns of the pollutant, spatial representativeness of the metrics, and correlation between each metric and copollutant concentrations. Overall, the use of metrics based on the US National Ambient Air Quality Standards (for example, the use of a daily 8-h maximum O3 as opposed to a 24-h average metric) was supported by this analysis. Comparative analysis of temporal metrics also provided insight into underlying relationships between specific air pollutants and respiratory health.

Introduction

Studies of the acute health effects of ambient air pollution have used various temporal metrics to characterize daily carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and fine particulate matter (PM2.5) concentrations. Commonly examined metrics include 24-h average concentrations, daily 1-h maximum concentrations, and daily 8-h maximum concentrations. Bell et al. (2005) showed that variation in O3 between cities and over time, differed by averaging time used to characterize the O3 concentrations (that is, 24-h average, 1-h maximum, and 8-h maximum). The choice of daily pollutant averaging time could likewise affect risk estimates observed in epidemiological studies. Some pollutant averaging times may show stronger associations with health outcomes, because they reflect a more biologically relevant exposure (for example, peak vs average exposure) or because they more strongly correlate with average population exposures compared with other temporal metrics. In addition, certain metrics could act as better surrogates for other, unmeasured pollutants responsible for the adverse health effects, such as certain metrics of CO and NO2 acting as surrogates of particles from traffic sources (Sarnat et al., 2001).

Temporal metrics that reflect peak pollution levels (for example, 1-h maximum) may be the most biologically relevant if the health effect is triggered by a high, short-term dose rather than a steady dose throughout the day. Peak concentrations, however, are frequently associated with episodic, local emission events, resulting in spatially heterogeneous concentrations across an urban area and, thus, prone to measurement error when using fixed-site concentrations as the estimate of exposure. As a result, a 24-h average concentration metric may often be more representative of average population exposures.

It is also possible that the most appropriate temporal metric for an epidemiological analysis is determined by exposure factors related to population time–activity patterns; some metrics may better capture average population exposures, because they include hours when the population is most likely to be exposed to ambient air. A relevant exposure time window for assessing health effects of traffic pollution, for example, may be during heavy commuting hours, when pollutant concentrations are highest and people are more directly exposed to ambient air. Alternatively, if centrally located monitoring stations, which are often used in epidemiological studies to characterize exposure, only reflect downtown concentrations, daytime hours might correlate best with personal exposures given the influx of people into the city center during the day and exodus at night. Conversely, nighttime exposure metrics incorporate hours when people are likely to be in their homes and less likely to be outdoors and exposed directly to ambient air, thus increasing exposure measurement error. As exposure measurement error can lead to attenuated effect estimates, if some pollutant metrics approximate population exposures better than other metrics, we would expect to see variation in epidemiological results according to choice of metric. Furthermore, if associations between nighttime concentrations and health outcomes are dramatically different from associations between daytime concentrations and health outcomes, this may indicate that a 24-h average metric is inappropriate, as it inherently combines both day and night concentrations into one metric. Lastly, if the use of different temporal metrics of air pollution leads to different results, metric choice could potentially explain differences in observed epidemiological associations across studies.

In our previous analyses of ambient air pollution levels and respiratory emergency department visits in Atlanta (Metzger et al., 2004; Peel et al., 2005; Sarnat et al., 2008; Tolbert et al., 2007; Sarnat et al., 2009), we presented results using a priori exposure metrics chosen for each pollutant of interest, based on the National Ambient Air Quality Standards and previous studies of air pollution and acute health effects (Sunyer et al., 1997; Zmirou et al., 1998; Ostro et al., 2001). These a priori exposure metrics included the daily 1-h maximum concentration for CO and NO2, the daily 8-h maximum concentration for O3, and a 24-h average for PM2.5. In the present study, we describe the variability of epidemiological results attributable to the use of different daily metrics of the same pollutant, comparing the results using our a priori pollutant metrics to those obtained using alternative temporal metrics of pollutant concentrations. The degree of sensitivity to the choice of metric is relevant to future investigations of air pollution and respiratory health, can offer clues to biological mechanisms, and ultimately can be used to inform regulatory policy.

Methods

Air Quality Data

We obtained hourly ambient concentrations of CO, NO2, O3, and PM2.5 from the US Environmental Protection Agency's Air Quality System as well as the Aerosol Research Inhalation Epidemiology Study monitor located near downtown Atlanta (Van Loy et al., 2000; Hansen et al., 2006). Data from all monitoring stations in the study area that provided hourly measurements were used to assess the spatial heterogeneity of each metric (described below). However, for the epidemiological models, we used measurements from a single, centrally located monitor for each pollutant. We obtained daily meteorological data from the National Climatic Data Center at Hartsfield–Atlanta International Airport. We chose to examine CO, NO2, and O3 because these pollutants were associated with respiratory emergency room visits in our previous analyses (Peel et al., 2005). We also assessed PM2.5 because our previous analyses were suggestive of an effect in spite of limited sample size (Peel et al., 2005). Furthermore, a motivation of the study was to explore whether alternative temporal metrics would yield stronger associations than our a priori daily metrics, which are metrics commonly used in air pollution studies. For each pollutant, we created the following temporal metrics of daily pollutant concentrations: a daily1-h maximum, a 24-h average, an average of commute hours (“commute,” 0700–1000 hours and 1600–1900 hours), a daytime average (“daytime,” 0800–1900 hours), a nighttime average (“nighttime,” 2400–0600 hours), and, for ozone only, a daily 8-h maximum. Within a pollutant, the study days included in the analysis were the same across metrics. The analytic time period differed by pollutant depending on the period of monitoring available at the central monitoring station: CO was examined from 1 January 1993 through to 30 June 2003; NO2 was examined between 1 March 1994 and 31 December 2004; O3 was examined from March through to October of every year between 1993 and 2004; PM2.5 was examined from 1 August 1998 through to 31 December 2004.

Emergency Department Data

Individual-level data from computerized billing records were obtained from 41 of 42 acute-care hospitals in the 20-county Atlanta area (50-mile radius). We examined daily counts of selected respiratory-related emergency department visits for patients living within any one of the 225 ZIP codes located wholly or partially in the 20-county Atlanta study area. Emergency department visits with a primary International Classification of Diseases 9th Revision (ICD-9) diagnostic code for asthma and wheeze (493, 786.09), chronic obstructive pulmonary disease (491, 492, 496), upper respiratory infection (460–466, 477), and pneumonia (480–486) were classified as respiratory emergency department visits. We excluded repeat visits by patients visiting the same hospital within a single day. There were 1,068,525 respiratory emergency department visits between 1993 and 2004, with an average of 244 visits per day.

Statistical Analysis

We modeled the association between air pollution and respiratory emergency department visits using a case-crossover framework, a special case of the time-series approach (Lu and Zeger, 2007). Using this time-stratified approach, referent days were chosen within the same calendar month and within the same maximum temperature category as the day of the respiratory emergency department visit (Schwartz, 2004, 2005; Zanobetti and Schwartz, 2006). For example, if the visit occurred in March 2000 on a day with a maximum temperature of 72°F (the case day), we selected all other days in March 2000 with a maximum temperature between 70°F and 75°F as the control days. Maximum temperature categories were in five degree increments and three degree increments at the extremes: <35°F or >89°F. Counts were then pooled across individuals within a hospital to create a time series of counts for each hospital. We chose to match on temperature rather than day-of-week because temperature effects are non-linear and can be challenging to adequately control in regression models compared with day-of week, which can be controlled using indicator variables.

We analyzed the data using Poisson generalized linear models, scaling the variance estimates to account for overdispersion. The model included indicator variables for day-of-week and holidays, cubic terms for 2-day moving average (lag 1–2) minimum temperature (same-day temperature was accounted for by matching) and for 3-day moving average (lag 0–2) of dew point temperature (cubic terms). We also repeated the analysis using the time-series models from our previously published work (Peel et al., 2005) to evaluate whether the observed patterns were sensitive to modeling approach. Briefly, our previous time-series models included cubic splines with monthly knots to control for temporal trends, seasonal indicator variables, and cubic splines to control for temperature and dew point temperature. All analyses were performed using SAS, version 9.2, statistical software (SAS Institute, Cary, North Carolina, USA).

Metrics Comparison

We calculated partial correlations (that is, correlations after controlling for the covariates included in the time-series models) between all of the metrics, both within and across pollutants. We compared the spatial heterogeneity of the metrics for each pollutant to assess whether some metrics might better reflect population-wide exposures (Ito et al., 2001); metrics that are more spatially representative (that is, more correlated across space) might better reflect personal exposures in the study population, thus reducing bias due to measurement error relative to other metrics. Thus, comparing the spatial heterogeneity of the metrics may shed light on any observed differences in strength of association with respiratory emergency room visits. To compare the spatial heterogeneity of the different averaging times for a given pollutant, we created the metrics at every air quality monitoring station in the study area that measured hourly concentrations. As these additional monitoring stations were located at various distances from the central monitor, we were able to assess the degree of correlation at various distances for each of the metrics.

We examined associations between daily respiratory emergency department visits and metrics of CO, NO2, O3, and PM2.5 concentrations on the previous day, lag 1. In the primary analyses, we chose to focus on previous day (lag 1) pollution concentrations, as this lag was consistently associated with the outcome in previous analyses (Peel et al., 2005). Owing to the uncertainty of the relevant lag period of exposure for the pollutants of interest, in sensitivity analyses, we also examined alternative lags of each metric (lag days 0, 2, 3). Lag 0 was defined as the period from midnight to midnight on the day of the visit; lag 1 was defined as the period from midnight to midnight on the day preceding the visit, and so on. To compare the magnitude of effect across different metrics of the same pollutant, we calculated risk ratios for both an interquartile range (IQR) increase in concentration, which differed across metrics, and for a standard unit increase, which was the same for all metrics of a given pollutant. The risk ratios for an IQR increase allowed for a comparison of effects for a similar degree of increase relative to each metric's distribution of concentrations, whereas the risk ratios for a standard unit increase allowed us to compare the magnitude of effect for an absolute increase (for example, 10 p.p.b.) in pollutant concentration. χ2-values and corresponding P-values, which are not unit dependent, were calculated to compare the strength of statistical association for each pollutant metric. χ2- and P-values, which are highly influenced by sample size, could be compared because the number of days included in the analysis was the same across metrics for a given pollutant.

Results

Descriptive statistics for each of the pollutant metrics examined are presented in Table 1. For a given pollutant, many of the metrics were highly correlated (Table 2). As expected, correlations were generally higher between overlapping temporal metrics; for example, the nighttime and daytime metrics were less correlated with each other than with the 24-h average, which encompassed both nighttime and daytime hours. For CO, correlations among the metrics ranged from 0.48 to 0.91, with the weakest correlation observed between the daytime and nighttime values. Correlations among NO2 metrics ranged from 0.44 to 0.90, with relatively weak correlations between the nighttime and daytime metrics and between the 1-h maximum and daytime metrics (r=0.45 and 0.44, respectively). The O3 metrics were more strongly correlated with one another (r=0.68–0.95) with the exception of the nighttime metric, which was uncorrelated with the other O3 metrics except for the 24-h average metric (r=0.46). Correlations among PM2.5 metrics ranged from 0.60 to 0.94. Similar to CO and NO2, the weakest correlation among the PM2.5 metrics was between daytime and nighttime.

Table 1: Descriptive statistics for air pollution data.
Table 2: Partiala spearman correlation coefficients for all air pollutant metrics.

Diurnal patterns for the traffic-related pollutants (CO, NO2, and PM2.5) were bimodal, with peaks during the morning and evening rush hours (Figure 1). Hourly maxima for CO and NO2 typically occurred at night between 2100 hours and 2300 hours. Concentrations of these pollutants remain elevated during much of the overnight period because of meteorology. Ozone also exhibited a typical diurnal trend, with peaks occurring in the mid- to late afternoon and minima occurring during the night.

Figure 1
Figure 1

Diurnal pattern* for selected pollutants. *Average of hourly values over study period, hour 1 refers to the hour between 2400 hours and 0100 hours.

Spatial Correlations of the Metrics

We examined the spatial correlation of various metrics to assess whether differences in spatial correlation between the metrics could explain differences in the observed associations. The spatial correlations between CO, NO2, O3, and PM2.5 metrics at the various monitoring station distances are shown in Figure 2. Nighttime O3 was the most spatially heterogeneous of the O3 metrics; all of the other O3 metrics showed strong spatial correlations even for long distances between monitors. Spatial correlations for NO2 were fairly similar across metrics for distances <20 km. However, in comparisons of monitors >38 km apart, the NO2 daily 1-h maximum was considerably more spatially heterogeneous than the daytime metric. Generally, metrics that included hours when NO2 concentrations were highest exhibited greater spatial heterogeneity (note that the monitoring station located at 15 km is impacted by a nearby freeway). Similarly, the PM2.5 metrics showed strong spatial correlations (r>0.7 for all distances) with the exception of the 1-h maximum, which was more spatially heterogeneous (0.5<r<0.6 between distances of 10 and 60 km). The 24-h average PM2.5 was the most spatially homogeneous of the PM2.5 metrics examined.

Figure 2
Figure 2

Spatial correlations for O3, NO2, PM2.5 and CO metrics.

Comparing Statistical Significance of Association

Risk ratios, 95% confidence intervals, χ2-values and corresponding P-values from the regression models are shown in Table 3. On the basis of the χ2-values, the nighttime metrics for both CO and NO2 were the most strongly associated with respiratory emergency department visits. The daytime metric for CO and NO2, which not only corresponds to hours of lower concentrations but also periods of time when people are more likely to be exposed, showed the weakest associations for these pollutants. With the exception of the daytime metrics, associations for the CO and NO2 metrics were all statistically significant.

Table 3: Risk ratios, 95% confidence intervals and χ2-values for associations between lag 1 air pollution metrics and respiratory emergency department visits.

The daily 1 and 8-h maximums yielded the strongest associations for O3. The daytime metric, which captured the hours of peak O3 concentrations, was also strongly associated with respiratory emergency department visits. The commute and the 24-h average metrics for O3, however, were only weakly associated with respiratory emergency department visits (P>0.05), and the nighttime metric of O3 was inversely associated with respiratory emergency department visits. Ozone was the only pollutant for which the choice of metric affected the direction of association. Given the negative correlations between the nighttime O3 metric and the CO and NO2 metrics (Table 2), we suspected that this negative association might be confounded by the positive associations observed with the various metrics of CO and NO2. In multi-pollutant models, when any of the CO and NO2 metrics were included in the model as covariates (with the exception of daytime CO), nighttime O3 was not negatively (or positively) associated with respiratory emergency department visits. There were no observed associations between any of the PM2.5 metrics and respiratory emergency department visits. However, the sample size was more limited for PM2.5, and all point estimates were above the null; χ2- values and risk ratios were comparable across metrics.

Table 4 displays the χ2-values and risk ratios (per interquartile range) for models using alternative lags of each metric (lag days 0, 2, and 3), in addition to the lag 1 day χ2-values as presented in Table 3. On the basis of the χ2-values, at shorter lags (0 and 1 days), the nighttime metrics for CO and NO2 were the strongest predictors of respiratory emergency department visits, whereas at longer lags (2 and 3 days), the χ2-values were similar among the nighttime, daily 1-h maximum, and 24-h average metrics. This result suggests that strong associations with the lag 1 nighttime metric may reflect associations with a longer lag of pollutant concentrations. For example, the nighttime metric included hours (2400–0600 hours) closest in time to the previous day compared with our other metrics of interest. For CO, NO2, and PM2.5, the nighttime metrics were the most strongly correlated to the previous day's concentrations, regardless of previous daytime metric chosen (Supplementary information Table A).

Table 4: χ2-values and risk ratiosa for associations between various lags of each pollutant metric and respiratory emergency department visits.

Comparing Magnitude of Association

Figure 3 displays the risk ratios and 95% confidence intervals scaled to each metric's IQR; correlations between our a priori metric (shaded) and alternative metrics are shown on the x-axis. For an IQR increase in each metric, risk ratios ranged from 1.004 to 1.015 for CO, 1.003 to 1.016 for NO2, 0.991 to 1.020 for O3, and 1.003 to 1.005 for PM2.5. When comparing the magnitudes of association, interpretation differed slightly according to how the regression coefficients were scaled: standard unit or IQR (Table 3). For example, the risk ratio estimate for nighttime NO2 was highest when effects were scaled according to the IQR, but the 24-h average risk ratio was highest when effects were scaled to the standard unit (10 p.p.b.). For CO, the 24-h average had the highest risk ratio for both scaling approaches, but the daily 1-h maximum was second highest when scaled to its IQR (1.4 p.p.m.), and was lowest when scaled to the standard unit (0.5 p.p.m.). The 1-h maximum, 8-h maximum, and daytime O3 metrics yielded higher risk ratios than the 24-h average, commute, and nighttime O3 metrics, regardless of scaling. In Figure 3 and Table 4 we only present the results scaled to each metric's IQR so that risk ratios can be compared for the same relative degree of variability.

Figure 3
Figure 3

Risk ratios and 95% confidence intervals* for associations between lag 1 pollutant metrics and respiratory emergency department visits. Partial Spearman correlations between a priori metrics (shaded in gray) and other pollutant metrics shown above x-axis. *RRs scaled to the interquartile range of each metric.

In sensitivity analyses using our previous time-series modeling approach (Peel et al., 2005), we observed similar patterns in risk ratios and χ2-values across the metrics. However, using this approach, the commute metrics for CO and NO2 were not significantly associated with respiratory emergency room visits.

Discussion

In this time-series analysis, we compared associations between various temporal metrics of daily ambient air pollution levels and respiratory health using a large data set of more than one million respiratory emergency department visits. Our motivation was to explore the implications of choice of pollutant temporal averaging time on health risk estimates within a time-series framework.

For a given pollutant, many of the metrics were strongly correlated, and yielded similar magnitude and statistical significance of associations with daily respiratory visits. As expected, pollutant metrics that were less correlated with each other exhibited larger differences in epidemiological associations than did correlated metrics. Differences in epidemiological results between metrics of the same pollutant may be due to (a) differences in biological relevance of the dose for the measured pollutant (for example, peak vs average exposures); (b) differences in metric spatial heterogeneity and corresponding representativeness of population exposures (exposure measurement error); (c) differences in correlation with personal exposures due to time-activity patterns (exposure measurement error); (d) differences in representing the true etiological agent (related to surrogacy); (e) differences in representing the relevant lag period of the pollutant (misspecified lag); (f) confounding by other pollutants during certain time-windows (for example, nighttime O3 with the NO2 and CO metrics); (g) model misspecification (for example, violation of linearity assumptions); or (h) random variation. Although some of these possible explanations were not directly testable in our study, we discuss them in the context of our findings below.

In general, variability in the observed results reflected pollutant diurnal patterns, with temporal metrics that included peak pollutant hours tending to show the strongest associations with respiratory emergency department visits and metrics capturing hours of low concentrations showing weaker associations. For example, O3 is formed during the daylight hours and is depleted at night; metrics incorporating the peak afternoon hours of O3 were correspondingly most strongly associated with our outcome. Conversely, NO2 is lowest during the daylight hours when it is being more rapidly dispersed and oxidized; during the evening and overnight hours NO2 is oxidized more slowly and the mixing height decreases, hence the concentrations increase. NO2 and CO metrics that included the hours of higher concentrations (including the 1-h maximum and nighttime metrics) showed stronger associations than did models using metrics that included concentration minima for these pollutants, despite the typical hours of peak concentration for these pollutants being late evening hours, when people are less likely to be outside.

Differences among the metrics were most pronounced for O3, wherein three metrics were strongly associated with the outcome (1-h maximum, 8-h maximum, and daytime), two metrics were weakly associated with the outcome (commute and 24-h average), and one metric was inversely associated with the outcome (nighttime). Nighttime O3 concentrations were negatively correlated with all of the CO, NO2, and PM2.5 metrics, likely due to the depletion of O3 by reaction with NO; when vehicle emission pollutant concentrations (that is, CO and NOx) are elevated at night, O3 depletion is high as well. We found that when we controlled for CO and NO2 concentrations in multi-pollutant models, nighttime O3 was no longer inversely associated with respiratory emergency department visits. The nighttime concentrations of O3 may serve as an inverse surrogate of traffic-related pollutants such as NOx. Fresh NO emissions scavenge ozone at night without the reverse process of NO2 photolysis that leads to O3 formation. Furthermore, lower mixing heights at night tend to increase NOx, CO, and PM2.5 concentrations. As NOx emissions are spatially heterogeneous, O3 scavenging at night is also spatially heterogeneous; in the more populated urban center, the higher NOx levels result in lower O3 levels at night. The negative association observed for nighttime O3 suggests that O3 is not the only pollutant linked to respiratory outcomes—an example of biological insights gained through assessment of alternative temporal metrics. Lastly, although investigators would be unlikely to choose a nighttime metric of O3 in an epidemiological study, this finding also argues against the use of a 24-h average metric for O3, which itself was only weakly associated with the outcome. In our analysis, inclusion of nighttime O3 concentrations within a 24-h average not only dilutes the relevant concentrations by adding irrelevant hours (that is, bias toward the null) but also includes hours when the relationship between O3 and respiratory emergency room visits may be negatively confounded by other pollutants.

In a previous study, Bell et al. (2005) compared air quality under seven emission scenarios using 1-h maximum, 8-h maximum, and 24-h average O3 concentrations to characterize air quality. Rankings of the different emission scenarios differed according to the metric of O3 chosen, but rankings based on the 1-h maximum and 8-h maximum were more similar to each other than to the rankings based on the 24-h average. In a panel study of asthma symptoms in 25 asthmatic children, associations using the 1-h maximum O3 metric were similar to those using the 8-h maximum (Delfino et al., 1998). Our O3 findings are consistent with these previous studies.

From an exposure stand point, our results highlight the potential for certain pollutant metrics to act as surrogates for other pollutant metrics. Our CO and NO2 findings, for example, indicate that CO or NO2 could be acting as a surrogate for the other, as shown by the correlations in Table 2 (for example, r=0.61 for 1-h max). Alternatively, CO and NO2 may be serving as surrogates of another pollutant, namely O3, as CO and NO2 metrics incorporating peak concentration hours were shown to be the most strongly associated with the outcome, and more strongly correlated with peak O3 concentrations than did other CO and NO2 metrics. Thus, associations between emergency department visits and peak (1-h maximum) NO2, a precursor of O3, may be partly confounded by O3 or vice versa.

Teasing out the effects of each pollutant through the use of multi-pollutant modeling is complicated by the differences in measurement error between the pollutants (Tolbert et al., 2007). Comparative analysis of temporal metrics within and across pollutants may provide an alternative approach for identifying the pollutant more likely to be the etiological agent. For example, NO2 metrics that were more strongly correlated with 8-h maximum O3 showed stronger associations with respiratory emergency room visits. This was also true for CO. Furthermore, the CO and NO2 metrics yielding the strongest associations were the hours when people were least likely to be exposed to ambient air (that is, night hours), and weaker associations were observed when people were more likely to be exposed to ambient air (that is, daytime and commute hours). If NO2 and CO were the true etiological agents, we might expect to observe associations for metrics incorporating hours when people are more likely to be exposed to ambient air, regardless of the correlation with 8-h maximum O3. These results suggest that the etiological agent is more likely to be O3 than CO or NO2.

Certain metrics may also serve as surrogates of the same pollutant but for a different lag. The nighttime metrics for previous day (lag 1) CO and NO2 showed some of the strongest associations with respiratory emergency room visits, despite night hours being some of the least likely hours of population exposure to ambient air. Perhaps the nighttime metric at lag 1 is more predictive of respiratory emergency room visits, because it acts as a better surrogate for pollution on earlier days (longer lags). Nighttime NO2 and CO concentrations were the best surrogates of NO2 and CO concentrations on the previous day regardless of metric, likely because nighttime hours (2400–0600 hours) were closer in time to the previous day (Supplementary Table A).

The comparison among PM2.5 metrics was less informative because we did not observe significant associations with the outcome of interest. However, the consistency of effect estimates across metrics provided reassurance that a strong association would not be missed if analysis was limited to the standard 24-h average metric. In our data, the spatial correlations of the PM2.5 metrics were also similar, except for the daily 1-h maximum, which was more spatially heterogeneous. Although in this analysis we did not assess specific chemical components of PM2.5, it should be noted that the spatial heterogeneity of PM2.5 can vary by the chemical composition of the particles (Wade et al., 2006). Few epidemiological studies have presented results for PM2.5 or PM10 using a temporal metric other than the 24-h average. We noted reports of three panel studies of asthmatic children investigating the relationship between PM10 and asthma symptoms that examined more than one averaging time for PM10. One study found modestly stronger associations using a 24-h average PM10 metric compared with a 1-h maximum (Ostro et al., 2001) and two studies showed slightly stronger associations using peak PM10 metrics (1-h maximum and 8-h maximum) compared with the 24-h average (Delfino et al., 1998, 2002).

This analysis highlights some of the challenges involved in comparing scaled risk ratios. We presented the risk ratios scaled to each metric's IQR; these risk ratios take into account the range of concentrations for each metric and provide a comparison for the same relative degree of variability. We also presented results for a standard unit (for example, 0.5 p.p.m.) so that results could be compared for the same absolute unit increases in concentration for each pollutant. However, comparisons based on absolute increases in concentration may be misleading in this setting in which daily temporal metrics of the same pollutant are being compared, as a 0.5 p.p.m. increase in daytime CO is a meaningfully greater relative increase compared with a 0.5 p.p.m. increase in 1-h maximum CO, for example. As a consequence of these differences among metrics in concentration variability, the metrics yielding the largest magnitude of effects often differed by the choice of scaling. In this analysis, we preferred the χ2-values to identify the strongest associations, as χ2-values are not affected by scaling. Comparing χ2-values across metrics was appropriate in this study because the sample size was the same for all metrics of a given pollutant.

In this analysis, we focused on respiratory-related emergency department visits and did not present results for cardiovascular disease visits. In our previous work, we found same-day pollution levels (lag 0) to be most strongly associated with cardiovascular emergency room visits (Metzger et al., 2004). Same-day pollution effects can be difficult to compare across temporal metrics because some of the averaging times include hours late in the day, potentially after the bulk of emergency room visits have occurred on a given day. In the present analyses of various metrics of lag 1 pollution, although temporality issues may still have a role (for example, the nighttime metric captured hours at a longer lag than did the daytime metric), all temporal metrics included concentrations before the emergency room visit occurred. These temporality and choice of lag issues are clearly important to the estimation of effects, as recently shown by Lokken et al. (2009).

In summary, we found that epidemiological results were generally similar across different temporal metrics of the same pollutant and would have led to similar conclusions about the relationship between the pollutant and respiratory emergency room visits. Exceptions included the nighttime O3 metric and the daytime metrics of CO and NO2. It would be of interest to know how well each of the time-averaged metrics correlate with measured personal exposures; studies in which personal exposures have been measured longitudinally could likely address this question without additional data collection. We found that our a priori metrics for CO (1-h maximum), NO2 (1-h maximum), and O3 (8-h maximum), based on the National Ambient Air Quality Standards and designed to capture peak concentrations, yielded associations that were as strong or stronger than the other metrics. Our analysis supports the use of these exposure metrics in future studies of ambient air pollution and respiratory health.

References

  1. , , and Metrics matter: conflicting air quality rankings from different indices of air pollution. J Air Waste Manag Assoc 2005: 55(1): 97–106.

  2. , , , and Symptoms in pediatric asthmatics and air pollution: differences in effects by symptom severity, anti-inflammatory medication use and particulate averaging time. Environ Health Perspect 1998: 106(11): 751–761.

  3. , , , , and Association of asthma symptoms with peak particulate air pollution and effect modification by anti-inflammatory medication use. Environ Health Perspect 2002: 110(10): A607–A617.

  4. , , , , , , , , , , , , , and Air quality measurements for the aerosol research and inhalation epidemiology study. J Air Waste Manag Assoc 2006: 56(10): 1445–1458.

  5. , , , and Monito-to-monitor temporal correlation of air pollution and weather variables in the North-Central US. J Expo Sci Environ Epidemiol 2001: 11(1): 21–32.

  6. , , , , , , and Air pollution and risk of stroke: underestimation of effect due to misclassification of time of event onset. Epidemiology 2009: 20(1): 137–142.

  7. , and On the equivalence of case-crossover and time series methods in environmental epidemiology. Biostatistics 2007: 8(2): 337–344.

  8. , , , , , , , , and Ambient air pollution and cardiovascular emergency department visits. Epidemiology 2004: 15(1): 46–56.

  9. , , , , and Air pollution and exacerbation of asthma in African-American children in Los Angeles. Epidemiology 2001: 12(2): 200–208.

  10. , , , , , , , , and Ambient air pollution and respiratory emergency department visits. Epidemiology 2005: 16(2): 164–174.

  11. , , , , , , , , and Fine particle sources and cardiorespiratory morbidity: an application of chemical mass balance and factor analytical source-apportionment methods. Environ Health Perspect 2008: 116(4): 459–466.

  12. , , , and Gaseous pollutants in particulate matter epidemiology: confounders or surrogates? Environ Health Perspect 2001: 109(10): 1053–1061.

  13. , , , , , , , and An examination of exposure measurement error from air pollutant spatial variability in time-series studies. J Expo Sci Environ Epidemiol, advance online publication, March 11, 2009; doi:10.1038/jes.2009.10.

  14. The effects of particulate air pollution on daily deaths: a multi-city case crossover analysis. Occup Environ Med 2004: 61(12): 956–961.

  15. How sensitive is the association between ozone and daily deaths to control for temperature? Am J Respir Crit Care Med 2005: 171(6): 627–631.

  16. , , , , , , , , , , , , and Urban air pollution and emergency admissions for asthma in four European cities: the APHEA Project. Thorax 1997: 52(9): 760–765.

  17. , , , , and Multipollutant modeling issues in a study of ambient air quality and emergency department visits in Atlanta. J Expo Sci Environ Epidemiol 2007: 17(Suppl 2): S29–S35.

  18. , , , , and Aerosol Research and Inhalation Epidemiology Study (ARIES): PM2.5 mass and aerosol component concentrations and sampler intercomparisons. J Air Waste Manage Assoc 2000: 50: 1446–1458.

  19. , , , , , , , , , and Effect of instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, Georgia. J Air Waste Manage Assoc 2006: 56: 876–888.

  20. , and Air pollution and emergency admissions in Boston, MA. J Epidemiol Community Health 2006: 60(10): 890–895.

  21. , , , , , , , , , , , , and Time-series analysis of air pollution and cause-specific mortality. Epidemiology 1998: 9(5): 495–503.

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Acknowledgements

This work was supported by grants from the Electric Power Research Institute (EP-P27723/C13172), the US Environmental Protection Agency (CR-83407301-0, RD833626, R82921301-0), and the National Institute of Environmental Health Sciences (R01ES11294).

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Affiliations

  1. Emory University, Atlanta, Georgia, USA

    • Lyndsey A Darrow
    • , Mitchel Klein
    • , Jeremy A Sarnat
    • , Matthew J Strickland
    • , Stefanie E Sarnat
    •  & Paige E Tolbert
  2. Georgia Institute of Technology, Atlanta, Georgia, USA

    • James A Mulholland
    •  & Armistead G Russell

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Correspondence to Lyndsey A Darrow.

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DOI

https://doi.org/10.1038/jes.2009.49

Supplementary Information accompanies the paper on the Journal of Exposure Science and Environmental Epidemiology website (http://www.nature.com/jes)

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