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Many previous studies of acute asthma exacerbations and ambient air pollution have examined effects of only a few of the many contaminants that are found in urban air, making it difficult to determine which specific air pollutant or group of pollutants is most important in triggering hospital visits. In particular, ambient particulate matter is usually characterized based only on mass concentration, despite the knowledge that many particulate matter components such as acidity, metals or different carbon fractions might have different effects on asthma morbidity. In addition, whereas numerous studies have reported associations between daily air pollution concentrations and counts of hospital visits for asthma or other respiratory diseases, few studies have evaluated whether risks for air pollution-related hospital visits vary across communities that differ in their baseline health status. To investigate these issues, we conducted the study reported below. The study's primary goals were to assess whether ambient air quality differed in two New York City locations and to relate daily variation in the ambient concentrations of various air contaminants to daily variation in acute asthma exacerbations in both communities. Mid-town Manhattan and the South Bronx are separated by less than 5 miles. However, the two regions of New York City differ greatly in levels of asthma morbidity. Although these differences are likely to be caused by multiple factors, including differential access to primary care for asthma, the present study was not designed to investigate these differences. Rather, we investigated whether day-to-day variations in air pollution were associated with asthma emergency department (ED) visits in each community and compared the magnitude of the air pollution effect between the two communities. To investigate this question, we analyzed daily counts of ED asthma visits to hospitals serving two distinct communities, one in Manhattan and the other in the South Bronx, and related those data to daily enhanced air monitoring data in each community. We analyzed air quality and weather data collected over about a two year period, from January 1999 through November 2000, at two centrally located measurement stations sampling a broad range of contaminants (Figure 1). In addition to data on many commonly measured chemical air pollutants, information was collected on several components of airborne particulate matter that had not previously been assessed for their possible association with asthma exacerbations. Emergency department data on asthma visits for the corresponding dates were collected from the 22 hospitals throughout New York that served the communities surrounding the air monitoring stations. Data for hospital patients who lived in zip code areas within approximately 1.5 miles of either measurement site were extracted, The study measured 24-hour average ambient air concentrations of acetone, aldehydes, chromium, iron, nickel, manganese, hydrogen ion, sulfate, pollen and mold spores. One-hour average concentrations were measured for ozone (03) sulfur dioxide (SO), nitrogen oxides (NO), number of particles measuring 0.007 to 2.5 micrometers, particulate matter <2.5 micrometers (PM2.5) and particulate matter < 10 micrometers (PMoj). Three-hour average concentrations were measured for PM2.5 elemental and organic carbon. The hourly data were used for calculating daily averages, maximum concentrations and, for ozone, eight-hour moving averages. Meteorological data (temperature, wind speed and direction, humidity) were also collected. Ambient air data were collected from one site in Manhattan from January 1999 through November 2000, from one site in the Bronx from January 1999 through August 1999 and from a second nearby site in the Bronx from September 1999 through November 2000. Table 1. Mean Concentrations of Air Pollutants and Bioaerosols Measured in Bronx and Manhattan. The values are summary statistics of all daily observations from January 1999 through November 2000, including days with missing values that were imputed by regression modeling for the time-series analysis of health data. Air Contaminant Bronx Manhattan Max 8-hour 03 (ppm)* 0.027 0.021 NO2 ppm)* 0.031 0.036 SOz ppm)* 0.011 0.012 PM (g/mS)* 14.5 16.6 Max PM2. ( g/m 3) 27.3 27.5 Coarse PM (pg/ 3)t 7.69 7.10 Sulfate (g/m)* 3.6 4.0 pH * 5.15 5.04 Elemental Carbon (tg/m3 1.19 1.32 Oranic Carbon m 3.17 3.09 Total Metals (ng/m)** r101 94.0 Total Aldehydes (/m3) 1 6.6 16.2 Total Pollen (#/m3) 22.3 13.2 Total Mold (#/m ) . 448 490 "* Mean levels significantly different l(P < 005, paired t-test) between the two communities over the entire study period. "** Nickel was significantly higher in Manhattan compared to Bronx over entire study period SCoarse PM (= PM10 - PM25) was not included in statistical comparisons of air quality in Bronx and Manhattan, but was included as a key pollutant variable in the asthma ED visit analysis SBronx was significantly higher than Manhattan for two of three pollen sub-categories; Manhattan was significantly higher than Bronx for one of seven mold spore sub-categories. Mean levels of PM2.5s, PM25. acidity, PM2s sulfate, PM25 nickel, acid gases, ammonia, sulfur dioxide and nitrogen oxides were significantly higher in Manhattan than in the Bronx over the entire study period (Table 1). Mean levels of ozone, ragweed pollen and grass pollen were significantly higher in the Bronx. Statistical tests had power to detect small mean differences because of large sample sizes. Therefore, although several mean comparisons were significantly different, the absolute differences in analyte concentrations between the two sites were generally not large. For example, for most comparisons, the higher mean was no more than about 1.6-fold larger than the lower meai, and many of the significant mean differences were less than 1.2-fold. Exploratory temporal analyses of certain air contaminants were conducted. PM 1 and PM2:, organic carbon and elemental carbon were evaluated by the hour and day of week. Both sites exhibited a daily temporal pattern in PM.0 and PM25 levels. Lowest levels were seen in the middle of the night (2 A.M.). The highest levels were seen in the morning, with a smaller peak in the early evening. Particulate matter elemental carbon concentrations peaked at 9 A.M. at both sites. The particulate organic carbon fraction increased modestly in concentration from early in the morning to a high in the evening for Manhattan, whereas the Bronx organic carbon levels remained nearly constant throughout the day. Acetone, elemental carbon, nitrogen oxides, PM10 and particulate Fe were the only variables showing a noticeable day-of-week trend, with somewhat lower daily means on Sundays, increasing through the week to Thursdays. Table 2. Relative Riskst and 95% Confidence Intervals for Asthma ED Visits as Function of 5-Day Mean Air Pollution and Bioaerosols from Single-Pollutant Models. Bold text indicates statistical significance at the 0.05 level. Pollutant Air Contaminant Bronx Manhattan Concentration Increment** Max 8-hour 03 1.06 (1.01, 1.10) 1.06 (0.94, 1.19) 0.024 Max 8-hour 03 (warm season) 1.08 (1.03, 1.12) 1.04 (0.91, 1.19) 0.024 N02 1.10 (1.01, 1.18) 0.95 (0.72, 1.25) 0.034 SOl 1.11 (1.06, 1.17) 0.99 (0.88, 1.12) 0.011 M215 1.05 (1.01, 110) 1.04(0.94, 1.15) 15.9 Max PM2.5 109 (1.03, 1.15) 1.04 (091, 1.18) 27.6 Coarse PM 1.02 (1.00, 1.04) 1.02 (0.98, 1.07) 7.4 Sulfate 1.03 (1.00, .06) 1.05 (0.98, 1.13) 3.9 pH 0.99 (0.98, 1.00) 0.99 (0.95, 1.02) 5.07 Elemental Carbon 1.04 (0.99, 1.09) 1.06 (0.94, 1.19) 125 rganic Carbon 1.05 (0.93, 1.17) 1.20 (0.96, 1.49) 3.14 otal Metals 1.02 (0.99, 1.05) 102 (0.91, 1.15) 93.5 otal Aldehydes 1.02 (1.00, 1.04) 1.03 (0.96, 1.10) 16.1 Total Pollen 1.00 (1.00, 1.00)t 1.01 (1.00, 1.02) 17.0 otal Mold 1.01(0.99, 1.03) 1.01 (0.97, 1,06) 504 "* A mean Relative Risk of 1.10 indicates that an increase in the daily pollutant concentration equal to the Pollutant Concentration Increment is associated, on average, with a 10% increase in daily asthma ED visits. "** Increment value used to calculate relative risks in Tables 2 and 3 were based on the mean pollutant level combining all data from both communities. Same units as in Table 1. tWhen RR and CI bounds appear equal, it is due to rounding. The air monitoring study was not designed to attribute air contaminant variability to particular sources. However, air mass back-trajectory analysis was used to compare local and long-distance transport contributions to total contaminant levels.' On an annual average basis, 39 - 47 percent of measured sulfate concentrations was associated with long-distance transport from the west and southwest of New York. In comparison, long-distance transport from those directions contributed 26 - 32 percent of PM2.5 and 11 -- 17 percent of sulfur dioxide. Nitrous acid (HONO) and ammonia levels appeared unrelated to long-distance air trajectories, suggesting that atmospheric transport did not contribute significantly to their concentrations. Mean daily crude rates of asthma ED Visits were over eight fold higher in the Bronx study area (16.9 per 100,000 persons) than in the Manhattan area (2.02 per 100,000 persons). Exploring reasons for these differences was beyond the scope of the present study. Among 14 key pollutants examined individually in regression analyses, five had statistically significant effects on asthma ED visits in the Bronx, including daily eight-hour maximum 03, mean daily NO2, SO2, PM5. and maximum one-hour PMi.5 (Table 2). No statistically significant pollution effects were observed in the Manhattan community. In two-pollutant and three-pollutant regression models, 03 and SO2, and to a lesser extent maximum one- hour PM2.5, were the most robust pollutants (Table 3). In other words, these pollutants exhibited less change in their effect estimates as additional pollutants were added to the models. it is of particular interest that we observed more robust health impacts of the daily maximum PM25 concentration than for the 24- hour mean, suggesting that peak exposures may have larger health impacts. In analyses restricted to the warm season (April through October), 03 effects in the Bronx were larger and more significant than in the full-year analysis, and they were approximately double those seen in Manhattan, suggesting greater susceptibility and/or exposure to this airway irritant and pro-inflammatory agent in the Bronx. Ozone effects in the Bronx also remained significant after removing daily maximum 8- hour average concentrations that exceeded the ozone National Ambient Air Quality Standard (NAAQS) from the data set (<1% of all observations). Analyses by sex suggested that the air pollution effects in the Bronx were greater among females than males. No strong differences in effects were observed with age strata, though there was some indication of larger effects in older adults, Our findings of significant air pollution effects in the Bronx, but not Manhattan, are likely to relate in part to greater statistical power for identifying effects in the Bronx where baseline ED visits were greater, but they may also reflect greater sensitivity to air pollution effects in the Bronx. For example, the mean effect estimates (expressed as relative risks) for the associations of average daily ozone with asthma ED visits were the same in the Bronx and Manhattan, although the Bronx relative risk was statistically significant, 1 Bari A, Dutkiewicz VA, Judd CD, Wilson LR, Luttinger D, Husain L. 2003. Regional sources of particulate sulfate, S02, PM2.5, HC1, and HNO3, in New York, NY. Atmospheric Environment 37: 2837-2844. Table 3. Relative Risks (95% Confidence Intervals) for Asthma ED Visits as Function of 5-Day Mean Air Pollution from Two-Pollutant Models. Note: Pollutants included here were those that were significant predictors of ED visits in single-pollutant models Exposure increments used to compute RRs were the same as in Table 2. Bold text indicates statistical significance at the 0.05 level. Contaminant Controlled with RR, Bronx RR, Manhattan Max 8-hour 03 PM2.5 1.06 (1,01, 1.10) .05 (0.93, 1.19) Max PM.s5 1.04 (1.00, 1.09) 1.05 (0.93, 1.19 NO2 1.05 (1.01, 1.10) 1 07 (0.94, 1.2 1) SO,2 1.05 (1.01 1.10) 1.06 (0.93, 1.20) PM2,s Max 8-hour 03 105 (1.01, 1.10 103 (0.94, .14 Max PM2.5 0.99 (0.92, 1.06) 1.04 (0.89, 1.23) NO 1.03 (0.98, 1.09) 1.08 (0.95, 1.23) SSO2 1.01 ( 0.96, 1.06) 1.05 (0.94, 1.17) Max PM2.5 Max 8-hour 03 . 1.07 (1.02, 1.13) 1.02 (0.89, 1.17) PM25, 1.09 (1.00, 1.20) 0.99 (0.79, 1.23) NO22 1.07 (1.01, 1.14) 1.10 (0.92, 1.3 1) SO2 1 .05 (099, 1.11) 1.05 (0.90, 1.21) NO? Max 8-hour 1.08 (1.0, 1.17 . 091 (0.68, 1.21) PM25 1.06 (0.97, 1.16) 0.83 (0.59, 1.17) Max PM25 1.04 (096, 1.14) 0.84 0.59, 1.20 SO2 1.02 (0.94, 1.12) 0.95 (0.69 1.30) SO, Max 8-hour 03 1.11(1.05, 117) . 099 (088, 1.12) PM25 11 1.04 118 . 0.97 (0.85, 1.1) Max PM5 .09 (1.03,1.16) . _ 0.98 (0.85, 1.12) NO 1..1 1.04, 1.17) 1. 01 87, 11 6 while the Manhattan estimate was not. In contrast, Bronx relative risks for average daily NO, and SO2 and maximum hourly PM2.5 were statistically significant in the Bronx and were also substantially larger than the corresponding Manhattan effect estimates. To evaluate the specificity of the air pollution effects observed for asthma visits; we analyzed the relationships between air pollutants and ED visits for outcomes thought a priori to be unrelated to air pollution (e.g., urinary tract infections, acute gastroenteritis). Of the five pollutants that had significant univariate effects on asthma in the Bronx, one, 24-hour PM25, had significant effects on the control outcome. Positive but non-significant effects were seen for the remaining pollutants, except ozone. There was no evidence of any effect of ozone on control ED counts. These results could suggest some degree of overestimating risk in the analysis. The observed associations between specific pollutants and asthma ED visits do not necessarily indicate cause and effect. It is possible that unmeasured confounders related to indoor environmental exposures or socio-economic status variables might be contributing to variability in acute asthma exacerbations. However, within each study area, the time-series design at least partially controls for unmeasured confounders because each case acts essentially as its own control. The analysis detects marginal changes in the outcome variable relative to the baseline rate that are associated with the measured exposure variables, and the baseline rate would include effects due to unmeasured variables, such as local or indoor exposures. Estimating exposure based on centrally located ambient monitors also adds some uncertainty to the results reported here due to potential exposure misclassification compared to actual personal exposure. However, the relatively high population density of the Bronx and Manhattan allowed for the central monitors to be used as an indicator for exposure for a relatively small area (i.e., the population residing within approximately 1.5 miles of the monitoring site). Furthermore, the correlation between the two monitoring sites was relatively high (i.e., greater than 0.6) and mean levels were very similar for most analytes, perhaps partially mitigating against exposure misclassification biases that might occur because of movement of residents throughout the greater New York City area. CONCLUSIONS AND RECOMMENDATIONS Mean levels of most air contaminants did not differ substantially between the two New York City monitoring sites over the course of the study. When differences were observed, mean levels in Manhattan tended to be modestly higher than mean levels in the Bronx for most pollutants. Mean ozone and pollen levels were somewhat higher in the Bronx. The health analysis results suggest that the criteria pollutants PM25, SO2, 03 and NO2 had a statistically detectable impact on acute asthma ED visits in a community with a relatively high baseline rate of acute asthma exacerbations. In two-pollutant and three-pollutant regression models, 03 and SO2, and to a lesser extent maximum one-hour PM2.5, were the most robust pollutants. Robust effects of 03 have been seen in previous ED asthma studies and in hospital admissions studies of asthma and other respiratory diseases. It is of particular interest that we observed more robust health impacts of daily maximum PM2.5 concentration than of the 24-hour mean, suggesting that peak exposures may have larger health impacts. The following recommendations are suggested based on the study results: I. EPA should consider the findings in this study and others identifying respiratory health effects associated with SO2 concentrations below current standards during their review of the SO2 NAAQS. The results of this study were submitted in response to a Call for Information issued by EPA in May, 2006 to initiate review of the SO? NAAQS. 2. Future time-series studies examining associations between ambient air pollutants and health outcomes would benefit from direct evaluation of the relationship between personal exposure and regional monitoring data. 3. More research should be conducted to try to determine if peak, short-term (e.g. hourly) elevated concentrations of PM.5 are more strongly associated than daily average concentrations with asthma and other health endpointst If the science is sufficiently strong, consideration should be given to the effects of short-term PM.5 excursions in future reviews of the particulate matter NAAQS. 4. The high correlations between pollutants (including components of PM2.5) make it difficult in these epidemiologic studies to confidently identify critical compounds. Alternative strategies to address this question should be considered in the fuiture. 5 Further evaluation of the statistical methods employed in time-series epideriological studies is warranted, based on the suggestion of possible model bias indicated by our analysis of control outcomes. 6. To the extent that targeted community based asthma interventions are planned with respect to air pollution messages, higher priority should be given to communities with larger asthma burdens.