The value of using seasonality and meteorological variables to model intra-urban PM$_{2.5}$ variation

Abstract

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$_{2.5}$ in El Paso, Texas. Monitoring was conducted at four sites purposely selected to capture the local traffic variability. Effects of meteorological events on seasonal PM$_{2.5}$ variability were identified. For instance, in winter low-wind and low-temperature conditions were associated with high PM$_{2.5}$ events that contributed to elevated seasonal PM$_{2.5}$ levels. Similarly, in spring, high PM$_{2.5}$ events were associated with high-wind and low-relative humidity conditions. Correlation coefficients between meteorological variables and PM$_{2.5}$ fluctuated drastically across seasons. Specifically, it was observed that for most sites correlations between PM$_{2.5}$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 PM$_{2.5}$.