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Predictive Dust Modelling for Mineral Planning

First published in the May 2015 issue of Quarry Management

John Bruce from Portsmouth University and Geoffrey Walton, managing director of DustScan Ltd, set out options for assessing the levels of dust and the likelihood of nuisance from dust coming from quarry operations, so as to meet the requirements of planners and regulators

Most quarrying companies are well aware of the complications that may arise from excessive dust from their workings and related activities, and of the need to take appropriate precautions.  These may have been specified in planning conditions or set out in an agreed Dust Management Plan. A range of techniques is used to control dust, including:

  • Fixed and mobile spays and mist cannons
  • Housing dusty activities and fixed plant, including conveyor transfer points
  • Covering loose materials with fabrics or by seeding
  • Cleaning hard-surfaced roads, vehicle washing and sheeting
  • Limiting vehicle speeds
  • Limiting areas of disturbance and using screens and advance planting in key locations
  • Proactive site management, especially near dust-sensitive neighbours and at the site entrance.


In spite of these measures, problems may still arise in proposed operations when assessing if, where and when dust emissions might occur beyond the site boundary. Hence, there may be a need, at the planning stage, to investigate the potential for fugitive dust or, in other words, to predict dust dispersion from the site.

Commonly, dust dispersion arises as a result of poor housekeeping, ie not carrying out the dust-control measures adequately, and as a result of adverse weather conditions. It is generally understood that significant dust dispersion may occur when there are dry conditions and high winds, especially when these winds are blowing towards sensitive receptors. Studies at the University of Portsmouth’s School of Earth and Environmental Sciences have explored meteorological controls on dust levels at quarries and earthworks where activities were relatively consistent. Investigations covered the levels of dust arriving at points beyond the site boundary. These results have allowed reasonable predictions of dust levels based on weather data – essentially, this is an empirical method. At the same time, the ability of computer software which models air quality to estimate dust dispersion has also been investigated, to see if there is an alternative route to assess the potential for fugitive dust. Planners and regulators usually appreciate such checks.

Empirical dust-dispersion modelling

The empirical method relies upon establishing relationships between dust levels which come from specific quarry sources and dust levels at identified dust-sensitive receptors, and how they vary with changes in weather conditions. The development of this technique relies upon directional dust monitoring at points along the pathway between principal dust sources and likely dust receptors, and particularly at the site boundary and the key receptor. Dust levels were reported as AAC% and EAC% values for seven-day monitoring periods. Figure 1 is a suggested matrix of potential dust impacts based on experience of general weather conditions and annoyance from dust in southern England. Wind speed, temperature and rainfall are the constraints considered, but the wind directions, barriers and distance between dust source and receptor are also relevant and such a table has to be used with care.

As a first step in the more detailed empirical approach to dust distribution, it is necessary to collect and plot data for the more extreme dust events along the pathway. Typically, the dust levels fall away from a dust-generating site and from such data it is possible to relate dust levels at a receptor to those at the site boundary. Other patterns may appear due to off-site activities. Most quarry managers recognize that off-site dust around a quarry is not necessarily from their own site. Dust can be generated by seasonal arable farming operations, and by other industrial activities, including those connected to, but not part of, the mineral working area, such as recycling and secondary processing. Usually it is possible to characterize dust by its source using directional dust samples and analytical techniques including microscopy, ICP-MS or SEM-EDX.

It is generally understood that dust propagation is promoted by high wind speeds, that rainfall suppresses dust generation and that high temperatures act to dry material surfaces and enhance dust production from exposed areas. To collect appropriate weather data it is important to establish a local weather station – low-cost electronic weather stations capable of measuring wind speeds and direction, rainfall and temperature are now readily available (see lead photo). This local, on-site approach may be preferred, as wind speeds and direction are much influenced by local topography and by ground and earthworks features associated with some quarries. Data from stations a number of kilometres away can be quite different. Meteorological data are collected at hourly intervals over the same seven-day monitoring periods as the dust data.

The two sets of data can then be used to explore relationships between weather and dust information using linear regression or optimization modelling. Figure 2 is a plot of a typical set of results. Initially, it shows measured EAC% values at a fixed position, in this case the site boundary between the centre of adjacent quarry operations and the potential receptor. Later on, measured and predicted dust levels are shown for subsequent periods. The model fits well and shows the periodicity in dusting relating to the recorded meteorological data. With this information and an understanding of the phasing of quarry workings, it is possible to assess the weather conditions likely to give rise to concerns and to plan for appropriate dust-control measures.

The whole process is suited to the collection of data over a three-to-six-month period, which is consistent with the time taken for the assembly of data for most planning applications and environmental impact assessments. It is best if this is undertaken between spring and autumn as this is when most dust problems arise.

Software-based dust modelling

Software-based modelling is often used to investigate air-quality issues surrounding gases including NOX and SOX, and fine dusts such as PM10. This modelling is commonly applied to stack emissions and industrial processes, but there are means by which dust emissions from activities in quarries can be accommodated. A widely used modelling package employed in the authors’ study is that developed by CERC and known as ADMS. As with most detailed software modelling, the input data is critical in achieving reliable results.

The input data typically includes:

  • Terrain or ground surface data
  • Characteristics of the potential dust sources, including particle size grading
  • Dust source locations, eg as a point, area or volume source
  • The rate and timing of dust emissions
  • Weather data, including wind speed and direction, rainfall, temperature and cloud cover
  • Sensitive receptor locations.

Dust emissions depend on actual dust-generating operations, but generalized data for dust emissions from typical site operations and ground conditions can be obtained from the US EPA and from the European Environment Agency (EEA). Research needs to be done to arrive at the actual dust-emission values based on site-specific monitoring and measurement. However, measured values for dust levels at locations within and outside the site boundary, based on directional dust monitoring, may be compared with those from ADMS modelling that has used assumed values from the US EPA and the EEA.

Figure 3 shows that with emission factors from the literature, dust concentration values may be predicted with only limited accuracy (r2 = 0.17 to 0.36), although average dust concentrations for the whole period were reasonably similar (93µg m-3 predicted vs 54µg m-3 at the boundary monitor). Figure 4, however, shows that when emission factors are back-analysed to replicate dust concentrations at one point, predictions of dust dispersion elsewhere are very good (r2 >0.88). By this means, backed up by appropriate dust monitoring, it is hoped that better, more accurate emission factors for different quarries and quarrying activities can be established in due course. Eventually, improved predictions will be obtained, but at present it still remains prudent to undertake some form of ground-truth dust and weather monitoring both before and after quarrying activities, not least because most large operations are extensions of existing works.


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