Austin Elena, Zanobetti Antonella, Coull Brent, Schwartz Joel, Gold Diane R, Koutrakis Petros
1] Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA [2] Department of Environmental and Occupational Health, University of Washington School of Public Health, Seattle, Washington, USA.
Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA.
J Expo Sci Environ Epidemiol. 2015 Sep-Oct;25(5):532-42. doi: 10.1038/jes.2014.45. Epub 2014 Jul 9.
Local trends in ozone concentration may differ by meteorological conditions. Furthermore, the trends occurring at the extremes of the Ozone distribution are often not reported even though these may be very different than the trend observed at the mean or median and they may be more relevant to health outcomes. Classify days of observation over a 16-year period into broad categories that capture salient daily local weather characteristics. Determine the rate of change in mean and median O3 concentrations within these different categories to assess how concentration trends are impacted by daily weather. Further examine if trends vary for observations in the extremes of the O3 distribution. We used k-means clustering to categorize days of observation based on the maximum daily temperature, standard deviation of daily temperature, mean daily ground level wind speed, mean daily water vapor pressure and mean daily sea-level barometric pressure. The five cluster solution was determined to be the appropriate one based on cluster diagnostics and cluster interpretability. Trends in cluster frequency and pollution trends within clusters were modeled using Poisson regression with penalized splines as well as quantile regression. There were five characteristic groupings identified. The frequency of days with large standard deviations in hourly temperature decreased over the observation period, whereas the frequency of warmer days with smaller deviations in temperature increased. O3 trends were significantly different within the different weather groupings. Furthermore, the rate of O3 change for the 95th percentile and 5th percentile was significantly different than the rate of change of the median for several of the weather categories.We found that O3 trends vary between different characteristic local weather patterns. O3 trends were significantly different between the different weather groupings suggesting an important interaction between changes in prevailing weather conditions and O3 concentration.
臭氧浓度的局部趋势可能因气象条件而异。此外,即使臭氧分布极端值处出现的趋势可能与均值或中位数处观察到的趋势有很大不同,且可能与健康结果更相关,但这些趋势往往未被报告。将16年期间的观测日分类为能体现当地每日显著天气特征的宽泛类别。确定这些不同类别内臭氧平均浓度和中位数浓度的变化率,以评估浓度趋势如何受到每日天气的影响。进一步研究臭氧分布极端值处的观测趋势是否有所不同。我们使用k均值聚类法,根据日最高温度、日温度标准差、日地面平均风速、日水汽压均值和日海平面气压均值对观测日进行分类。基于聚类诊断和聚类可解释性,确定五类聚类解决方案是合适的。使用带惩罚样条的泊松回归以及分位数回归对聚类频率趋势和聚类内的污染趋势进行建模。确定了五个特征分组。在观测期内,每小时温度标准差大的天数频率下降,而温度偏差小的较温暖天数频率增加。不同天气分组内的臭氧趋势显著不同。此外,对几个天气类别而言,第95百分位数和第5百分位数处的臭氧变化率与中值的变化率显著不同。我们发现,臭氧趋势在不同的当地特征天气模式之间存在差异。不同天气分组之间的臭氧趋势显著不同,这表明主要天气条件变化与臭氧浓度之间存在重要的相互作用。