Modelling Dust Emissions from a Source Using Dust Monitoring and Meteorological Data

Abstract

This paper describes a study into the development of more robust dust emission factors by means of dust and meteorological monitoring. Emission factors for nuisance dusts in the literature are scarce, with estimates of dust output given for many processes in mass per unit area per year. Temporal variations and the extent and conditions in which maximum concentrations occur can therefore be impossible to predict with any accuracy. This investigation aims to improve predictions by “back calculating” emission levels based on dust monitoring around known dust sources. Nuisance dust and meteorological monitoring has been undertaken at a sand and gravel quarry in the UK for a consecutive period of two years. Sticky pad directional dust monitors were used to collect dust at eight locations at and around the site with meteorological data collected at an electronic weather station within the site. Air quality modelling software (ADMS) was used to test emission factors from the European Environment Agency (EEA) and the US Environmental Protection Agency (EPA) for emissions from mineral workings. Predictions were compared with the dust monitoring data to assess accuracy, with results showing limited poor correspondence (r2 < 0.3). Trends showed that emission predictions were poorest in winter; this is likely because most emission calculations are not weather dependent and seasonal fluctuations will occur. Dust emission rate calculations were altered with respect to the dust monitoring data for one monitoring location on the mineral site boundary and the model was run again. Results were then tested at two different locations up to 200 m from the site boundary, with very positive correlations (r2 > 0.89) and similar maximum concentrations (<5% difference). This study has therefore shown that accurate site-specific emission rates can be produced in combination with site boundary sticky pad dust monitoring in order to accurately derive estimations elsewhere.