Developing dust prediction modelling techniques through sticky pad dust monitoring

  • John Bruce

Student thesis: Doctoral Thesis

Abstract

Coarse dust, often referred to as fugitive or nuisance dust, has no statutory standard sampling methods or limit values and there are no widely used coarse dust dispersion methodologies. The current assessment of coarse dust impacts in the UK typically uses a risk-based approach set out in established guidance (e.g. IAQM, 2016, 2018) that accounts for site activities, average weather conditions and distances and directions to receptor locations. An alternative method uses atmospheric dispersion models to predict coarse dust dispersion in a similar method to that frequently used for gaseous pollutants such as NO2. However, detailed source inputs are needed for coarse dust modelling, which are challenging to accurately predict or to validate using dust monitoring. This thesis therefore aims to produce a suitable, simple and repeatable methodology for modelling coarse dust dispersion in the UK and adjusting it based on routinely available dust monitoring data.
A new dust monitoring dataset was acquired from a UK minerals site and existing emission factors from the literature were tested and adjusted using monitoring data, demonstrating that published emission rates are not suitable for predicting dust dispersion in isolation. The collection of dust monitoring data is therefore fundamental to predicting how coarse dust emissions vary at each site, so that emission rates can be ‘back calculated’ or adjusted for use in model predictions. An evaluation of the efficiency of a dust monitor was therefore undertaken to allow dust measurements to be converted for comparison with modelling outputs, providing a fundamental platform that enables all future modelling work to be adjusted appropriately, with an average dust catch of 7% suggested for use in calculating airborne dust concentrations.
A third study was undertaken that demonstrates the techniques developed in action, with a significant programme of dust monitoring and characterisation undertaken at a UK steelworks used to adjust and refine dust modelling emissions. A final study uses a significant existing dataset to establish a definitive, flexible protocol for undertaking future dust monitoring and efficiently utilising it for the adjustment of dust dispersion modelling predictions. It demonstrates that a variety of dust monitoring points, at different distances and in different cardinal directions away from each dust source location are the most practical, with best practice suggestions set out. The protocol is designed so that it can be used at any dust generating site, and will enable better estimates of dust impacts at additional locations where monitoring is not possible. This research is therefore a significant step forward in using dust monitoring to adjust coarse dust modelling as a viable technique. Recommendations for future improvements of the methodology include the need to analyse how each revised emission rate can be related to parameters such site activity, rainfall and temperature, and to understand efficiency relationships with other frequently used dust monitors
Date of AwardJan 2022
Original languageEnglish
SupervisorJim Smith (Supervisor) & Mike Fowler (Supervisor)

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