Dataset Information

The value of using seasonality and meteorological variables to model intra-urban PM2.5 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 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.

SUBMITTER: Olvera Alvarez HA

PROVIDER: S-EPMC6166668 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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The value of using seasonality and meteorological variables to model intra-urban PM2.5 variation.

Olvera Alvarez Hector A HA Myers Orrin B OB Weigel Margaret M Armijos Rodrigo X RX

Atmospheric environment (Oxford, England : 1994) 20180308

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</su ...[more]

PMID: 30288136

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Dataset Information

The value of using seasonality and meteorological variables to model intra-urban PM2.5 variation.

Publications

The value of using seasonality and meteorological variables to model intra-urban PM<sub>2.5</sub> variation.

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