Modelling PM2.5 concentrations and spatial correlation with weather conditions and adult asthma in north Texas in 2014

Abstract

The questions to be answered by this study are whether weather conditions correlate with PM2.5 concentrations, and whether there is a correlation between asthma-related hospital admissions and PM2.5 concentrations. The focus is on the Dallas-Fort Worth area of Texas—comprised of Collin, Dallas, Denton, Ellis, Erath, Hood, Hunt, Johnson, Kaufman, Navarro, Palo Pinto, Parker, Rockwall, Somervell, Tarrant, and Wise Counties—during the year 2014. A radial basis function neural network, via the Matlab Neural Network toolbox, is used to create a model to estimate the PM2.5 concentrations in the DFW area. Spatial statistical techniques are used to analyze the spatial autocorrelations among asthma-related hospital admissions. Spatial statistical analysis reveals that asthma-related hospital visits are concentrated in urban centers. Further statistical analysis results indicate that daily average PM2.5 concentration is positively correlated with daily maximum temperature, daily average station pressure, daily average wind speed, and daily sustained wind speed. Research indicates that the results concerning wind speed may be related to drought. The results also indicate that daily average PM2.5 concentration is negatively correlated with daily precipitation and daily average relative humidity when precipitation days are considered alone; however, when both precipitation and non-precipitation days combined are considered, there is no correlation between precipitation and PM2.5 concentration. Daily asthma-related hospital visits are weakly positively correlated with daily average PM2.5 concentrations when days with precipitation are considered, but weakly negatively correlated when non-precipitation days are considered alone.