Olvera Alvarez Hector A, Myers Orrin B, Weigel Margaret, Armijos Rodrigo X
School of Nursing, University of Texas at El Paso, 500 W. University Ave. El Paso TX, 79968 USA.
Health Sciences Center, University of New Mexico, Albuquerque NM USA.
Atmos Environ (1994). 2018 Jun;182:1-8. doi: 10.1016/j.atmosenv.2018.03.007. Epub 2018 Mar 8.
A yearlong air monitoring campaign was conducted to assess the impact of local temperature, relative humidity, and wind speed on the temporal and spatial variability of PM in El Paso, Texas. Monitoring was conducted at four sites purposely selected to capture the local traffic variability. Effects of meteorological events on seasonal PM variability were identified. For instance, in winter low-wind and low-temperature conditions were associated with high PM events that contributed to elevated seasonal PM levels. Similarly, in spring, high PM events were associated with high-wind and low-relative humidity conditions. Correlation coefficients between meteorological variables and PM fluctuated drastically across seasons. Specifically, it was observed that for most sites correlations between PM and meteorological variables either changed from positive to negative or dissolved depending on the season. Overall, the results suggest that mixed effects analysis with and as fixed factors and meteorological variables as covariates could increase the explanatory value of LUR models for PM.
开展了为期一年的空气监测活动，以评估当地温度、相对湿度和风速对德克萨斯州埃尔帕索市颗粒物（PM）时空变化的影响。监测在特意选定的四个地点进行，以捕捉当地交通的变化情况。确定了气象事件对季节性PM变化的影响。例如，在冬季，低风速和低温条件与导致季节性PM水平升高的高PM事件相关。同样，在春季，高PM事件与高风速和低相对湿度条件相关。气象变量与PM之间的相关系数在不同季节大幅波动。具体而言，观察到对于大多数地点，PM与气象变量之间的相关性要么从正变为负，要么根据季节消失。总体而言，结果表明，以[未提及的两个因素]为固定因素、气象变量为协变量的混合效应分析可以提高土地利用回归（LUR）模型对PM的解释价值。
