An Heuristic Prediction Method for Managing Environmental Blast Noise Impacts

The aim of this work is to manage adverse environmental impacts from long-range blast noise. The work was carried out as part of ongoing research at the DNV Spadeadam Testing and Research site (STaR). STaR carries out crucial major hazards work including improving safety concerns within industry decarbonization sectors and government agencies. The site performs a variety of explosives testing, resulting in environmental blast noise at off-site residential locations. The site is surrounded by complex topography, with terrain featuring range-dependent ground impedance and thermal properties which in turn effects the local meteorology. While accurately modelling blast wave propagation through such environments using traditional computational methods is a computationally expensive task, the required complex and rapidly varying meteorological data are not adequately available. To address this deficiency, a data-driven heuristic method is proposed for the prediction of blast noise levels at several sensitive receivers ranging from 4-14km. The model is formed from a preliminary dataset of off-site blast noise measurements, correlated with a multivariate array of available meteorological data. A principal component analysis is used to determine the atmospheric features which are most influential to sound propagation, and predict the likely range of peak sound pressure levels expected. It is concluded that useful predictions over time scales from an hour to a number of days can be obtained for managing environmental blast noise impacts, and that further measurements of blast noise, along with further correlations with measured atmospheric conditions, could improve the performance of the model.