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 PM2.5 in El Paso, Texas. Monitoring was conducted at four sites purposely selected to capture the local traffic variability. Effects of meteorological events on seasonal PM2.5 variability were identified. For instance, in winter low-wind and low-temperature conditions were associated with high PM2.5 events that contributed to elevated seasonal PM2.5 levels. Similarly, in spring, high PM2.5 events were associated with high-wind and low-relative humidity conditions. Correlation coefficients between meteorological variables and PM2.5 fluctuated drastically across seasons. Specifically, it was observed that for most sites correlations between PM2.5 and meteorological variables either changed from positive to negative or dissolved depending on the season. Overall, the results suggest that mixed effects analysis with season and site as fixed factors and meteorological variables as covariates could increase the explanatory value of LUR models for PM2.5.

中文翻译：

---

使用季节性和气象变量模拟城市内 PM 2.5 变化的价值

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 PM2.5 时空变化的影响。监测是在四个特意选择的地点进行的，以捕捉当地的交通变化。确定了气象事件对季节性 PM2.5 变异的影响。例如，在冬季，低风和低温条件与 PM2.5 高事件相关，导致季节性 PM2.5 水平升高。同样，在春季，高 PM2.5 事件与大风和低相对湿度条件相关。气象变量与PM2.5的相关系数跨季节波动剧烈。具体而言，据观察，对于大多数站点，PM2.5 之间存在相关性。5 和气象变量根据季节从正变为负或消失。总体而言，结果表明，以季节和地点为固定因素，气象变量为协变量的混合效应分析可以增加 LUR 模型对 PM2.5 的解释价值。