首页> 外文期刊>Journal of the air & waste management association >The Steubenville Comprehensive Air Monitoring Program (SCAMP): Associations among fine particulate matter, co-pollutants, and meteorological conditions
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The Steubenville Comprehensive Air Monitoring Program (SCAMP): Associations among fine particulate matter, co-pollutants, and meteorological conditions

机译:Steubenville综合空气监测计划(SCAMP):细颗粒物,共污染物和气象条件之间的关联

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We determined 24-hr average ambient concentrations of PM2.5 and its ionic and carbonaceous components in Steubenville, OH, between May 2000 and May 2002. We also determined daily average gaseous co-pollutant concentrations, meteorological conditions, and pollen and mold spore counts. Data were analyzed graphically and by linear regression and time series models. Multiple-day episodes of elevated fine particulate matter (PM2.5) concentrations often occurred during periods of locally high temperature (especially during summer), high pressure, or low wind speed (especially during winter) and generally ended with the passage of a frontal system. After removing autocorrelation, we observed statistically significant positive associations between concentrations of PM,., and concentrations of CO, NOx, and SO2. Associations with NO, and CO exhibited significant seasonal dependencies, with the strongest correlations during fall and winter. NOx, CO, SO2, O-3, temperature, relative humidity, and wind speed were all significant predictors of PM2.5 concentration in a time-series model with external regressors, which successfully accounted for 79 % of the variance in log-transformed daily PM2.5 concentrations. Coefficient estimates for NOx and temperature varied significantly by season. The results provide insight that may be useful in the development of future PM2.5 reduction strategies for Steubenville. Additionally, they demonstrate the need for PM epidemiology studies in Steubenville (and elsewhere) to carefully consider the potential confounding effects of gaseous co-pollutants, such as CO and NOx, and their seasonally dependent associations with PM2.5.
机译:我们确定了2000年5月至2002年5月之间在俄亥俄州Steubenville的24小时PM2.5及其离子和碳质组分的平均环境浓度。我们还确定了每日平均气态共污染物浓度,气象条件以及花粉和霉菌孢子数。数据以图形方式通过线性回归和时间序列模型进行分析。细颗粒物(PM2.5)浓度升高的多日发作通常发生在局部高温(尤其是夏季),高压或低风速(尤其是冬季)期间,并且通常随着额叶的通过而结束系统。去除自相关后,我们观察到PM浓度与CO,NOx和SO2浓度之间的统计学显着正相关。与一氧化氮和一氧化碳的关联表现出明显的季节性依赖性,在秋季和冬季具有最强的相关性。在具有外部回归的时间序列模型中,NOx,CO,SO2,O-3,温度,相对湿度和风速都是PM2.5浓度的重要预测指标,该模型成功占对数转换后方差的79%每日PM2.5浓度。 NOx和温度的系数估计值随季节而变化很大。结果提供了有用的见识,可能对Steubenville未来PM2.5减排策略的开发有用。此外,他们证明了在Steubenville(及其他地区)进行PM流行病学研究的必要性,以仔细考虑气态共污染物(如CO和NOx)的潜在混杂效应,以及它们与PM2.5的季节性相关关系。

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