Sarnat J A, Schwartz J, Catalano P J, Suh H H
Department of Environmental Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA.
Environ Health Perspect. 2001 Oct;109(10):1053-61. doi: 10.1289/ehp.011091053.
Air pollution epidemiologic studies use ambient pollutant concentrations as surrogates of personal exposure. Strong correlations among numerous ambient pollutant concentrations, however, have made it difficult to determine the relative contribution of each pollutant to a given health outcome and have led to criticism that health effect estimates for particulate matter may be biased due to confounding. In the current study we used data collected from a multipollutant exposure study conducted in Baltimore, Maryland, during both the summer and winter to address the potential for confounding further. Twenty-four-hour personal exposures and corresponding ambient concentrations to fine particulate matter (PM(2.5)), ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide were measured for 56 subjects. Results from correlation and regression analyses showed that personal PM(2.5) and gaseous air pollutant exposures were generally not correlated, as only 9 of the 178 individual-specific pairwise correlations were significant. Similarly, ambient concentrations were not associated with their corresponding personal exposures for any of the pollutants, except for PM(2.5), which had significant associations during both seasons (p < 0.0001). Ambient gaseous concentrations were, however, strongly associated with personal PM(2.5) exposures. The strongest associations were shown between ambient O(3) and personal PM(2.5) (p < 0.0001 during both seasons). These results indicate that ambient PM(2.5) concentrations are suitable surrogates for personal PM(2.5) exposures and that ambient gaseous concentrations are surrogates, as opposed to confounders, of PM(2.5). These findings suggest that the use of multiple pollutant models in epidemiologic studies of PM(2.5) may not be suitable and that health effects attributed to the ambient gases may actually be a result of exposures to PM(2.5).
空气污染流行病学研究使用环境污染物浓度作为个人暴露的替代指标。然而，众多环境污染物浓度之间的强相关性使得难以确定每种污染物对特定健康结果的相对贡献，并引发了批评，即由于混杂因素，对颗粒物的健康影响估计可能存在偏差。在当前研究中，我们使用了在马里兰州巴尔的摩市进行的一项多污染物暴露研究在夏季和冬季收集的数据，以进一步解决混杂的可能性。对56名受试者测量了24小时个人暴露以及细颗粒物（PM2.5）、臭氧、二氧化氮、二氧化硫和一氧化碳的相应环境浓度。相关性和回归分析结果表明，个人PM2.5与气态空气污染物暴露通常不相关，因为178个个体特异性配对相关性中只有9个具有显著性。同样，除了PM2.5在两个季节均有显著关联外（p < 0.0001），任何污染物的环境浓度与其相应的个人暴露均无关联。然而，环境气态浓度与个人PM2.5暴露密切相关。环境O3与个人PM2.5之间的关联最强（两个季节均为p < 0.0001）。这些结果表明，环境PM2.5浓度是个人PM2.5暴露的合适替代指标，并且环境气态浓度是PM2.5的替代指标而非混杂因素。这些发现表明，在PM2.5的流行病学研究中使用多污染物模型可能不合适，并且归因于环境气体的健康影响实际上可能是PM2.5暴露的结果。
