Ito K, Thurston G D, Nádas A, Lippmann M
Nelson Institute of Environmental Medicine, New York University School of Medicine,Tuxedo 10987, USA.
J Expo Anal Environ Epidemiol. 2001 Jan-Feb;11(1):21-32. doi: 10.1038/sj.jea.7500144.
Numerous time series studies have reported associations between daily ambient concentrations of air pollution and morbidity or mortality. Recent personal exposure studies have also reported relatively high longitudinal correlation between personal exposures to particulate matter (PM) and home outdoor PM concentrations, lending support to the health effects reported in time series studies. However, the question remains as to how well the temporal fluctuations in the air pollution levels observed at an outdoor monitor represent the temporal fluctuations in the population exposures to pollution of outdoor origins in a city, and how such representativeness affects the size and significance of risk estimates. Also, such spatio-temporal correlations would vary from pollutant to pollutant, likely influencing their relative significance of statistical associations with health outcomes. In this study, we characterized the extent of monitor-to-monitor correlation over time among multiple monitoring sites for PM less than 10 microm (PM10), gaseous criteria pollutants, and several weather variables in seven central and eastern contiguous states (IL, IN, MI, OH, PA, WI, and WV) during the study period of 1988-1990. After removing seasonal trends, the monitor-to-monitor temporal correlation among the air pollution/weather variables within 100-mile separation distance in these areas could be generally ranked into three groups: (1 ) temperature, dew point, relative humidity (r>0.9); (2) O3, PM10, NO2 (r: 0.8-0.6); and (3) CO, SO2 (r<0.5). Using the subsets for separation distance less than 100 miles, regression analyses of these monitor-to-monitor correlation coefficients were also conducted with explanatory variables including separation distance, qualitative (land use, location setting, and monitoring objectives) and quantitative (large and small variance) site characteristics, and region indicators for Air Quality Control Region (AQCR). The separation distance was a significant predictor of monitorto-monitor correlation decline especially for PM10 and NO2 (approximately 0.2 drop over 30 miles). Site characteristic variables were, in some cases, significant predictors of monitor-to-monitor correlation, but the magnitude of their impacts was not substantial. Regional differences, as examined by AQCR, were in some cases (e.g., in Metropolitan Philadelphia) substantial. In these areas, the pollutants that had generally poor monitor-to-monitor correlation in the overall seven states data (i.e., for SO2 and CO) showed higher monitor-to-monitor correlations, comparable with PM10 and O3, within the AQCR. These results are useful in interpreting some of the past time series epidemiological results. The differences in monitor-to-monitor correlations found across pollutants in this work (i.e., r approximately 0.8 vs. r approximately 0.4) are sufficiently large that they could be a factor in the different pollutant significance levels reported in the epidemiologic literature. It is recommended that future epidemiological studies collect and incorporate information on spatial variability among air pollutants in the analysis and interpretation of their results.
许多时间序列研究报告了每日空气污染的环境浓度与发病率或死亡率之间的关联。近期的个人暴露研究也报告了个人对颗粒物(PM)的暴露与家庭室外PM浓度之间存在较高的纵向相关性,这为时间序列研究中报告的健康影响提供了支持。然而,一个问题仍然存在:在室外监测器上观察到的空气污染水平的时间波动在多大程度上代表了城市中人群对室外来源污染的暴露的时间波动,以及这种代表性如何影响风险估计的大小和显著性。此外,这种时空相关性会因污染物而异,可能会影响它们与健康结果的统计关联的相对显著性。在本研究中,我们描述了1988 - 1990年研究期间,七个中部和东部相邻州(伊利诺伊州、印第安纳州、密歇根州、俄亥俄州、宾夕法尼亚州、威斯康星州和西弗吉尼亚州)多个监测点之间,小于10微米的颗粒物(PM10)、气态标准污染物以及几个气象变量的监测器间相关性随时间的变化程度。去除季节性趋势后,这些地区距离在100英里以内的空气污染/气象变量之间的监测器间时间相关性通常可分为三组:(1)温度、露点、相对湿度(r>0.9);(2)臭氧、PM10、二氧化氮(r:0.8 - 0.6);(3)一氧化碳、二氧化硫(r<0.5)。使用距离小于100英里的子集,还对这些监测器间相关系数进行了回归分析,解释变量包括距离、定性(土地利用、位置设置和监测目标)和定量(方差大小)的站点特征以及空气质量控制区域(AQCR)的区域指标。距离是监测器间相关性下降的一个重要预测因子,特别是对于PM10和二氧化氮(在30英里内相关性大约下降0.2)。在某些情况下,站点特征变量是监测器间相关性的重要预测因子,但其影响程度不大。通过AQCR检验的区域差异在某些情况下(例如在费城大都会区)很大。在这些地区,在七个州的总体数据中监测器间相关性通常较差的污染物(即二氧化硫和一氧化碳)在AQCR内显示出与PM10和臭氧相当的较高监测器间相关性。这些结果有助于解释过去一些时间序列流行病学结果。在这项工作中发现的不同污染物之间监测器间相关性的差异(即r约为0.8对r约为0.4)足够大,以至于它们可能是流行病学文献中报告的不同污染物显著性水平的一个因素。建议未来的流行病学研究在分析和解释结果时收集并纳入空气污染物空间变异性的信息。