Empirical modelling of directional dust from industrial sites

This paper describes a preliminary investigation into relationships between stickypad dust data and meteorological factors at two industrial sites. Site A is a construction site near the coast of the Caspian Sea where dust problems are anticipated due to strong winds from the north. Site B is a small sand and gravel quarry in central England, where dust movements towards the north east are monitored due to the proximity of a sensitive receptor. It was chosen due to the flexibility available for dust monitoring and for contrast with site A. At both sites dust samples were collected on an array of sticky-pad directional dust monitors. Samples were sealed and scanned for dust coverage (AAC%) and dust soiling (EAC %). Each site also had a weather station, such that results could be examined in relation to rainfall, wind conditions and temperature. For this exercise, samples were selected on the basis of their exposure to background dust, in order to reduce influence from anthropogenic dust sources workings but allow for further work once basic principles are determined. Models were developed via a correlation matrix between all weather measurements and the relevant temporal dust level. The strongest correlations were established, and linear regression was used to explore potential coefficients. Rainfall parameters included daily & weekly rainfall, as well as factored rainfall based on immediacy. Temperature measurements were averaged over the dust monitoring periods and compared with monthly dust trends. Increases in dust were observed at site A when temperatures remained high, so a constant was created which reflected this. A unique ‘wind-risk’ constant was established with relation to wind direction, strength and frequency. Both site models rely heavily on wind speeds from the appropriate direction, but site A also had strong seasonal fluctuations based on temperature. The final models were made using linear regression to incorporate all relevant parameters to form an effective representation of the dusting patterns observed. Improvements being considered include refining dust predictions to include site activities and adaptation to additional sites.