Field-testing a new directional passive air sampler for fugitive dust in a complex industrial source environment

Abstract

Quantifying the sources of fugitive dusts on complex industrial sites is essential for regulation and effective dust management. This study applied two recently-patented Directional Passive Air Samplers (DPAS) to measure the fugitive dust contribution from a Metal Recovery Plant (MRP) located on the periphery of a major steelworks site. The DPAS can collect separate samples for winds from different directions (12 × 30° sectors), and the collected dust may be quantified using several different measurement methods. The DPASs were located up and down-prevailing-wind of the MRP processing area to (i) identify and measure the contribution made by the MRP processing operation; (ii) monitor this contribution during the processing of a particularly dusty material; and (iii) detect any changes to this contribution following new dust-control measures. Sampling took place over a 12-month period and the amount of dust was quantified using photographic, magnetic and mass-loading measurement methods. The DPASs are able to effectively resolve the incoming dust signal from the wider steelworks complex, and also different sources of fugitive dust from the MRP processing area. There was no confirmable increase in the dust contribution from the MRP during the processing of a particularly dusty material, but dust levels significantly reduced following the introduction of new dust-control measures. This research was undertaken in a regulatory context, and the results provide a unique evidence-base for current and future operational or regulatory decisions.

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Environmental impact

Fugitive emissions from dusty industrial processes, unpaved roads, or stored aggregates are difficult to measure and abate. This is particularly problematic on large industrial complexes where identifying the often numerous fugitive sources is essential for regulation and effective dust management. This paper describes the first field deployment of a recently-patented Directional Passive Air Sampler (DPAS) at a large steelworks. The DPAS system could identify different fugitive dust sources around a specific processing operation, and also measure a reduction from these sources following the introduction of new controls. The DPAS is the only unpowered sampler that separately channels and collects pollutants from different wind sectors onto a medium permitting further characterisation. It is therefore invaluable to air quality practitioners.

1. Introduction

After the transport sector, industrial processes are the second greatest source of particulate matter (PM) in the UK,¹ accounting for an estimated 27% of PM₁₀ (particles with an aerodynamic diameter <10 µm) emissions in the UK in 2009.² Industrial emissions include ‘controlled sources’, where PM is released into the atmosphere via conduits such as stacks or chimneys, and also ‘fugitive sources’ where un-conduited PM are emitted to the atmosphere. Typical industrial sources of fugitive PM include: aggregate handing operations where materials are ground, crushed or abraded; wind suspension from uncovered materials on conveyors, vehicles or stockpiles; and the movement of vehicles on unpaved roads. Fugitive emissions therefore occur over relatively large areas, and their emission rates depend on meteorological factors such as wind speed and strength, and rainfall. As such, fugitive emissions are difficult to quantify and abate, and their contribution is often estimated in emission inventories or modelling studies.^1–3 Fugitive emissions can cover a range of particle sizes from <1 µm to 100 µm and are therefore referred to here as dust.

Quantifying the sources of fugitive dusts on complex industrial sites is essential for regulation and effective dust management. On a large industrial site such as a steelworks there are many potentially dusty operations associated with either steel-making or the related by-products. These different processes are often separately regulated and must comply with the specific permit issued by the appropriate regulator, such as the Environment Agency for England. Moreover, these separate processes may also be managed by different operators. The attribution and apportionment of fugitive dust is therefore essential to assess compliance with the appropriate permit and/or appropriate operator. Source attribution is also necessary to optimise dust control measures such as: water-spraying during aggregate movement or crushing; dampening down on haul routes; wheel-washing; screens on hoppers or conveyors; and/or growing vegetation to reduce windblown dust from bare areas.

Directional sampling is needed to resolve fugitive dust emissions in complex source environments. There are 3 standard methods routinely used for regulatory monitoring in England: the BS 1747 Parts 1 and 5 British Standard, or CERL-type, directional gauge; the Directional Frisbee gauge; and the Warren Spring Laboratory (WSL) wedge dust flux gauge.⁴ The BS 1747 directional gauge is a passive sampler that comprises of 4 collection pots positioned at right-angles to each other. It is the preferred method for routine monitoring,⁴ despite being shown to be fundamentally unsuitable for dust collection.⁵ The Directional Frisbee gauge comprises a standard Frisbee deposit gauge linked to meteorological equipment so that collection occurs only when the wind is from a predefined direction arc. The Directional Frisbee gauge performs far better than the BS 1747 gauge, however the need for extra meteorological equipment and power limits application for routine perimeter monitoring.⁴ The WSL wedge dust flux gauge is a passive sampler that traps particles in a foam filter. It can either rotate in the wind and sample dust from all directions, or, operate in a fixed position to sample dust from a predefined direction arc. To sample directionally, a pair of gauges is required; one omnidirectional gauge recording total flux, and another fixed gauge pointed at the point of interest.⁵ To resolve the dust contribution from multiple wind directions, several fixed gauges, each pointing in the appropriate direction would be required. The gauge has a relatively high collection efficiency, and is the preferred method for investigation requiring accurate results.⁴

As an alternative to these standard methods for directional monitoring, the University of Leeds, UK, has developed DustScan, a passive monitoring system that directionally collects dust on a sticky pad wrapped around a cylindrical sampling head.⁶ At the end of the monitoring period, the sticky pad is removed, and scanned on a flat-bed scanner. The dust content of each 15° sampling arc is then quantified as a percentage soilage. Testing of the DustScan system has shown that this sampling method is useful in nuisance dust situations,⁶ and is superior to the BS 1747 directional gauge.⁷ However, the dust cannot readily be removed from the sealed sticky pad,^8,9 and this could limit further characterisation (e.g. chemical, magnetic) of the sample. Also, there is the potential for cross-sampling between arcs because flows from different directions are not channelled separately.

A need therefore exists for an unpowered directional air sampler that can efficiently collect and resolve dust from multiple wind sectors, on a medium that permits further characterisation, for example by chemical or magnetic techniques. The Directional Passive Air Sampler (DPAS) was co-invented by Lancaster Environment Centre and the Environment Agency (Patent Number US 8,413, 527 B2) and consists of a fixed sampling carousel with 12 × 30° directional sectors, that is located within a rotating outer shell.^10,11 The DPAS rotates in the wind, and separately channels the dust flux associated with the 12 directional sectors into 12 horizontally-positioned vials containing foam filters (Fig. 1). The 12 dust samples can then be quantified and/or characterised in various ways.

Fig. 1 The new DPAS prototype modified to collect dust. (i) An expanded view of the sampler showing: (A), the rotatable upper outer shell; (B & C), the upper and lower parts of the fixed sampling carousel which is divided in 12 × 30° sectors; (D), the rotatable lower outer shell; and, (E), the rotatable bearing housing that connects the sampler to the stand. (ii) A cross-sectional view of the sampler showing; (F), the mouth of the sampler; (G), one of the 12 sampling channels; and, (H), the exit tail of the sampler. The passage of air through the sampler for a particular 30° sector is shown by the red arrow; the horizontally-positioned sampling vials are located on a small stand within the sampling channel.

The original DPAS prototype has been successfully deployed to measure nitrogen dioxide (NO₂) concentrations in urban settings,¹² and undergone laboratory testing for PM.¹³ This study describes the application of a redesigned version of the DPAS to sample fugitive industrial dust. As such: Section 2.1 summarises the redesign; Section 2.2 describes the study area and the regulatory context; and Section 2.3 presents 3 experimental methods to quantify the fugitive dust: photographic; mass-loading and magnetic. Section 3 presents the experimental application of the DPAS to sample fugitive dust on a large industrial complex, specifically to: (i) identify and measure the contribution made by a processing operation; (ii) monitor this contribution during the processing of a particularly dusty material; and (iii) detect any changes to this contribution following new dust-control measures. It discusses the advantages and limitations of the DPAS and considers how the DPAS may be deployed alongside other monitors to measure the atmospheric concentrations of PM₁₀. Section 4 concludes the work and offers a forward look.

2. Materials and methods

2.1. DPAS design and modifications made for sampling dust

The DPAS can be divided into a fixed carousel (Fig. 1B and C) and a rotating outer shell (Fig. 1A, D and E). Air enters the sampler at the mouth (Fig. 1F), flows through the air duct into one of 12 × 30° sampling channels (Fig. 1G), and then out through the tail. The fin ensures the sampler faces into the wind, which in turn ensures the air flows through the appropriate 30° sampling channel.

In the original DPAS prototype designed for sampling NO₂, each sampling channel contained only one, vertically positioned vial.¹¹ Initial field testing at the steelworks captured only small amounts of dust using vertical vials. The sampler was therefore redesigned to create a longer sampling channel in which a horizontally positioned vial (containing polyurethane foam) could be located to enable dust to be captured via impaction, rather than deposition. Field-testing has shown that these changes have improved the collection efficiency for dust. Moreover, wind-tunnel testing has shown that the new design has an increase in air-flow through the sampler, and a better response at low wind speeds. Specifically, the original DPAS internal wind speed was ∼70%, of the exterior wind speed; the current DPAS is ∼88%. The original DPAS could respond to a change in wind direction from <1 m s⁻¹ (90° angle from the wind direction) and fully line up with the wind at around at least 5.8 m s⁻¹ (10° from the wind direction); the current DPAS has a start-up and line-up speed of 2.3 m s⁻¹ at 10° from the wind direction.

Additionally, the longer sampling channels can now accommodate other sampling medium such as denuder tubes, and the sampler is currently being field-tested for ammonia.¹⁴ This widens the scope for future DPAS applications.

2.2. Study area and regulatory context

Sampling took place on a major steelworks complex located in eastern England. The steelworks site covers approximately 7 km², and contains many industrial operations associated with steel-making or the related by-products. Accordingly the steelworks site is a complex source environment with conduited emissions from: blast furnaces; coke ovens; sinter plant stacks; and other smaller-scale combustion and/or stacks; and fugitive emissions from: aggregate handing operations such as metal recovery; wind suspension from uncovered materials on conveyors, vehicles or stockpiles; and the movement of vehicles on unpaved roads. The steelworks site is regulated by the Environment Agency who provide specific permits for the different processes and/or operators. Understanding the source contribution made by the different processes and operators is therefore essential for effective air quality management.

The UK must comply with European Ambient Air Quality Directives designed to protect human health. For particulates, the annual mean PM₁₀ concentration must not exceed 40 µg m⁻³, and the daily mean PM₁₀ concentration should not exceed 50 µg m⁻³ more than 35 times a year. At a nearby air quality monitoring site (Fig. 2a), down-prevailing-wind of the steelworks complex, the daily PM₁₀ standard was exceeded for every year between 2006 and 2011 inclusive.¹⁵ Consequently, reducing particulate emissions from the steelworks site is of key importance, and the operators have been working with the Environment Agency and the Local Authority to identify and manage emissions sources. A recent dispersion modelling study showed fugitive emissions from aggregate handling, wind suspension of uncovered material, and suspension of material from unpaved roads surfaces to be the greatest contributor to PM₁₀ measured at the air quality monitoring site.³

Fig. 2 (a) Map showing the location of the Metal Recovery Plant (MRP) relative to the main steelworks complex, the nearby town, and the air quality monitoring stations. (b) Schematic showing the land-uses and fugitive dust sources in and nearby the Metal Recovery Plant (MRP). The operational permit for the MRP is shown by the dashed line which follows the line of the haulage roads. All emission sources enclosed by the dashed line are the responsibility of the MRP operator. Those emissions sources outside the dashed line are the responsibility of different operators. (c) Schematic showing the different sampler source sectors referred to in the text. See (b) for information on land-use.

The experimental sampling campaign described here focused on a Metal Recovery Plant (MRP) located on the northeast periphery of the steelworks (Fig. 2a) and 1 km south of the air quality monitoring site. The plant reprocesses aggregate by-products from steel-making and is potentially a major source of fugitive dust. Early in 2012, the MRP operator notified the Environment Agency of their intention to reprocess a significant amount of a different type of aggregate, skimmer material. Springtime is a particular problem for exceedances at the air quality monitoring site and the MRP plant is a known source of fugitive emissions. Moreover, skimmer material is particularly dusty for it arrives at the MRP at a temperature too high for safe water-dampening. As such, the Environment Agency requested a sampling campaign during the reprocessing of the skimmer material in order to provide the evidence base for any dust-control decisions required by the regulator. Additionally the sampling would benefit the MRP operator by providing quantitative evidence on fugitive sources, therefore enabling them to optimise their dust control.

There are several known sources of fugitive particulate emissions on and nearby the MRP site. These are shown on Fig. 2b and include: the stockpiling area where the by-product from the steelmaking process is stored and initially processed using ‘drop-balling’ methods; the Recip. Hopper, where the by-product material is loaded at the start of the processing; conveyors; the end silos where the processed material is loaded into uncovered aggregate-carry trucks; the unpaved access roads; and the windrows of stored aggregate material. Several different types of material are processed at the MRP including; slag, ballast, skimmer, and vessel bricks.

2.2.1. Details of sampling campaign. A schematic of the sampling area is shown in Fig. 2b and c. Several DPASs were deployed for various periods and locations during the sampling campaign; this study focuses on 2 DPASs located at positions A and C that were operational before and during the skimmer reprocessing. Two factors influenced their location; (i) they were deployed up and down-prevailing-wind of the processing area to determine the NET contribution from the MRP; (ii) the study area is a busy industrial site and therefore the DPASs needed to be located where they did not obstruct the daily MRP operations, and could be safely and conveniently accessed during fieldwork.

Sampler A was located on a grass bank upwind of the MRP processing area, and downwind of the main steelworks area. The DPAS separately channels air into 12 × 30° sectors (Section 2.1), and it was hypothesised that for Sectors A3 and A4 (Fig. 2c), this sampler would measure dust fluxes representative of the prevailing westerlies, before they cross the MRP. These winds will contain particulates emitted over the main steelworks complex, and from the town located to the SW (Fig. 2a). In contrast, Sector A11 would be representative of air passing over the MRP processing area with south-easterly winds. Sampler C was located on a 5 metre-high grass verge between two unpaved access roads. Sectors C3 and C4 are down-prevailing-wind of the MRP processing area, and therefore can be compared with Sectors A3 and A4 to estimate the NET contribution of the MRP. The source area of Sector C11 is predominantly grass-covered wasteland or farmland (Fig. 2c), with some local windrows of processed aggregate material. The C11 Sector can therefore be assumed to be broadly representative of the dust fluxes before the MRP and therefore be compared with A11 to calculate a south-easterly estimate for the NET contribution.

Sampler C also marks the westerly boundary of the MRP and is therefore significant in a regulatory context; all fugitive sources to the east of Sampler C are not the responsibility of the MRP operator. According to the permit provided by the Environment Agency, the MRP operator is responsible for only emissions within the boundary shown on Fig. 2b.

Samplers were deployed for 9 successive periods between March 2012 and April 2013; these deployment periods ranged from 29 to 57 days and were numbered 1 to 9 (Table 1). As detailed in Section 2.1, plastic vials containing foam were located in horizontally positioned in each of the 12 wind sectors. These vials were changed and replaced every 6 weeks, and brought to Lancaster University for analysis.

Table 1 Summary of the results and metadata for the 9 deployments. The Normalised Dust Signal for DPAS C, Deployment 8 is shown in 2 shades of grey; details are given in the text. Precipitation data for Deployment 9 are currently unavailable. The production chart for each deployment shows, from left to right, the amount of: slag, ballast and skimmer material processed. For the new dust controls: N = no controls; T = transitory period; and Y = all new dust controls in place

Deployment	1	2	3	4	5	6	7	8	9
Date	13/03–23/04/12	23/04–31/05/12	31/05–17/07/12	17/07–28/08/12	28/08–11/10/12	11/10–27/11/12	27/11/12–23/01/13	23/01–21/02/13	21/02–10/04/13
Met. data
Run of Wind (1000 km)
Monthly precip. (mm)	79	96	39	198	39	68	145	120	nnn
MRP data
Production chart kt
% skimmer	4	5	6	11	15	7	11	12	15
New dust controls	N	N	N	N	T	T	Y	Y	Y
DPAS data
Mass-loading A mg
Mass-loading C mg
Normalised Dust Signal A (mg kt^−1 km^−1) × 10^−3
Normalised Dust Signal C (mg kt^−1 km^−1) × 10^−3
Exceedances	6	0	0	2	4	1	1	0	5

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During the sampling campaign, the MRP operators introduced several new dust controls over a period of approximately 3 months. These included: optimising the timing of water-spaying to maximise dust-abatement at the entrance to the Recip. Hopper; curtains on the exit of the Recip. Hopper and on the end silos to reduce blowing dust; increased water-spraying on unpaved roads, and in tipping and stockpiling areas; extra man-power specifically to clear dust; raising awareness of dust and air quality issues amongst all staff members.

2.3. Experimental methods to measure particulates

Three separate methods were used to consider the amount of dust sampled in each of the 12 × 30° sectors.

2.3.1. Photographic. A 12 mega pixel Nikkon D300 and a light box (for shadowless lightening) were used to capture an image of the dusty vials following each deployment. The vials were placed with their dusty surface facing upwards and organised by their compass position.

2.3.2. Mass-loading. The vials were weighed before and after their deployment using a Mettler Toledo Balance; mass was measured in mg. To remove the influence or atmospheric moisture, samples from later deployments were dried in an oven before weighing. Before this drying process was introduced, some very clean samples (e.g. in Deployment 4) occasionally recorded negative mass-loading values. Mass-loading is frequently used as a measure of particulate mass from deposition samplers such as the Frisbee Gauge, or the British Standard Deposit Gauge which record mass-loading in mg m⁻² d⁻⁴.

2.3.3. Magnetic. Particulate emissions, particularly those from industrial processes such as fossil fuel combustion or steel-making invariably contain magnetic particulates.¹⁶ Magnetic analysis can be used to quantify the ambient PM₁₀ concentration in the atmosphere,¹⁷ and for source attribution.¹⁸ The latter requires a series of sophisticated magnetic tests which is beyond the remit of this paper; this study instead focuses on determining whether magnetism is a useful measurement technique for dust collected by the DPAS. As such, a single magnetic test was undertaken; each vial was placed in a 300 mT direct current magnetic field using a Molspin pulse magnetizer, and the room-temperature remanent magnetism (IRM₃₀₀) was measured using an Agico JR6 Spinner Magnetometer.

2.4. Normalised Dust Signal

The dust quantity collected in a particular vial for a particular deployment depends mainly upon the meteorology and amount of material processed by the MRP. The DPAS is a cumulative flux sampler, and therefore the amount of air which passes through each 12 × 30° sector depends on the wind speed and direction during the deployment; this is termed the Run of Wind (km). The main fugitive emission sources (Section 2.2) are strongly dependant on the MRP processing rates for at higher production levels there is more: material deposited into the Recip. Hopper; material on the conveyors; material loaded from the end silo into trucks; and, vehicle movement on the unpaved roads. To enable inter-comparison between periods of different meteorology and production rate, a Normalised Dust Signal metric was calculated. This is the amount of dust sampled in each vial (mg), per Run of Wind (km), per throughput of material processed (kt).

Meteorological data were obtained from an air quality monitoring station located 2.4 km due west of the MRP on the western boundary of the steelworks complex (Fig. 2a). The site is well-exposed, and provides a good representation of the broader wind field. Meteorological data could not be used from the nearest air quality station located to the northeast of the MRP; this site is sheltered on 3 sides, with a wind distribution strongly skewed in 2 directions, and an average annual wind speed of 0.6 m s⁻¹. These conditions of wind speed and direction are not typical of the broader wind field. The total amount of precipitation during the deployment period may also affect the amounts of particulates sampled. The closest rainfall gauge with data from the complete sampling period is located at Snitterby School Lane (SRC ID 56835), 18 km south of the steelworks complex. Data were obtained from the Met Office MIDAS database.¹⁹

Information on the daily production rate at the MRP was kindly provided by the operator.

3. Results and discussion

3.1. Preliminary results from different measurement methods

This is the first application of the DPAS to measure dust, and the first application of the photographic, mass-loading and magnetic methods to measure samples collected by the DPAS. It is therefore useful as a preliminary to consider whether: (i) the DPASs are operating well in the field; and (ii) the analysis methods are appropriate for the current study, in order to ensure we are sampling the intended source areas shown on Fig. 2c.

Fig. 3 shows an overlay of the 3 measurement methods for DPAS A and DPAS C for the second 6 week deployment (Table 1). This was selected for it is the first deployment with a complete dataset. The photographic image provides an immediate visual directional understanding of the sources and relative quantities of fugitive dust. DPAS A has captured dust from a range of directions, but the darkest staining is associated with the southwest quadrant (Sectors 2 & 3), which is down-prevailing wind of the steelworks complex. DPAS C also collected fugitive dust from a range of directions, with the darkest stains associated with vials to the east (Sectors 3–5) towards the MRP processing area; and to the west-northwest (Sectors 9 & 10), where the source area contains aggregate in windrows, ∼10 m from the sampler. Both samplers also have very clean vials; for DPAS A these are Sectors 9 and 10, which are somewhat sheltered by the inclination of the grass bank where the sampler is located. For DPAS C these are Sectors 11 and 12, whose source area is mostly grass-covered wasteland or farmland beyond the boundary of the steelworks complex.

Fig. 3 Overlay of the 3 measurement methods to estimate the amount of dust captured within each of the 12 sampling vials in DPAS A and DPAS C. Photographic images for the 12 pots are positioned around the periphery; mass-loading and magnetic remanence results are shown in the centre. Annotations show the land-use of key source sectors.

The mass-loading results (mg) are shown via the vertical axis on Fig. 3. These correlate well with the photographic image; for DPAS A, there are peaks in mass to the north, southwest and southeast, and a minima to west-northwest. For DPAS C there are peaks towards the east, and west-northwest. DPAS C which is located closest to the MRP processing operations contained the most dust.

The remanent magnetism following the application of an IRM₃₀₀ (× 10⁻⁸ Am²) field is shown on the horizontal axis. The ratio of the mass-loading to the magnetic remanence is approximately the same for all sectors and both DPASs except for the southwest quadrant of DPAS A. Here there is a very strong magnetic remanence compared to the mass-loading. This suggests that some or all of this cumulative dust has a different origin to dust sampled by DPAS C, and for the other directions of DPAS A.

This overview of the results indicates that the DPASs are operating well for: (i) the photographic image and mass-loading results correlate well with known sources of fugitive dust; (ii) the presence of clean vials adjacent to dust-laden vials (e.g. DPAS C, Sectors 10 & 11) shows the sampler has useful directional resolution. This is particularly important for a regulatory perspective for the MRP operators are only responsible for fugitive emissions within their boundary. Emissions from other sources areas such as the main steelworks complex (A1–A7) or from aggregate in windrows (C9 and C10) are the responsibility of other site operators. These preliminary results clearly show the DPASs are sampling the intended source areas shown on Fig. 2b.

The 3 analysis methods are in broad agreement on the main sources of fugitive dust, but each has different advantages and limitations. A photograph image provides an immediate directional visual snapshot of the main sources of directional dust. It can be taken quickly, including whilst on-site, and this gives the industrial operator and/or regulator very rapid evidence on which to base any current or future operational decisions. However, the photograph is 2 dimensional, and therefore does not provide an account of any dust that has penetrated into the sampling foam. Moreover, it is not currently quantifiable, and is therefore unsuitable for determining the NET contribution of the MRP, or for detecting any changes to dust levels following new control measures. Longer-term it is hoped that scanning software may be used to provide a quantitative value for the surface dust coverage of each vial, similar to the percentage soilage used by the DustScan system;⁶ although as discussed previously, this measurement method does not consider any dust that has penetrated further into the foam.

Mass-loading considers the dust both on the surface of, and within the sampling foam and gives a quantitative value that can be compared for different time periods and sampling locations. It is quick to process, and can therefore provide the industrial operator/regulator with rapid evidence for future operational decisions. As noted in Section 2.3, the samples must be dried before weighing to remove the influence of atmospheric moisture.

The combination of mass-loading and magnetic remanence is useful, for it indicates at least 2 separate sources of fugitive dust at the MRP. Magnetic remanence is also useful in areas of low dust, for only a small sample (down to trace levels) is required to detect an IRM. Unfortunately, one measurement of magnetic remanence (IRM₃₀₀) is not a good proxy for estimating the dust flux for as Fig. 3 shows, different dust sources have different magnetic strengths.

However, by undertaking a suite of magnetic analyses, the magnetic properties of the dust in each vial can be characterised. Magnetic analyses are highly sensitive, and can distinguish between different industrial sources,¹⁶ and/or different combustion processes, e.g. vehicular versus industrial.¹⁷ For example, these techniques were applied recently at Port Talbot steelworks to characterise the magnetic fingerprint of several key industrial processes.¹⁸ A similar study on this steelworks site could confirm that dust sampled in Sectors C3–C5 came from the MRP, and determine the origin(s) of the incoming dust sampled in the southwest quadrant of DPAS A.

The current study requires a quantitative measure of fugitive emissions to: (i) identify and measure the contribution made by the MRP; (ii) monitor this contribution during the processing of the skimmer material; and (iii) detect any changes to this contribution following new dust-control measures. As discussed above, the photographic image does not provide quantitative data, and magnetic remanence is not a useful proxy for cumulated dust flux. Therefore the following Sections use mass-loading measurements, which were used to calculate the Normalised Dust Signal, as outlined in Section 2.4.

3.2. Measuring the fugitive dust contribution from a reprocessing operation

3.2.1. Identifying the dust contribution made by the MRP. Section 2.2.1 hypothesised that the fugitive dust contribution made by the MRP could be estimated by comparing Sectors C3/C4 with A3/A4 for westerly winds, and A11 with C11 for south-easterly winds. To enable the inter-comparison between periods of different meteorology and production rate, the Normalised Dust Signal is used (Section 2.4).

Table 2 shows the Normalised Dust Signal and NET Normalised Dust Signal for 3 deployments (2, 4, 7) that represent 3 separate processing periods: before substantial skimmer processing; during skimmer processing but before new dust-controls; and, during skimmer processing and after new dust-controls. Deployments 2, 4 and 7 were selected for they characterise the 3 processing periods, and they represent deployments with complete datasets; results for all 9 deployments are shown in Table 1. For each of the 3 periods, the sampler downwind of the MRP processing area (C3, C4 and A11) records a greater Normalised Dust Signal, indicating that the amount of dust passing through the downwind sampler is greater than for the upwind sampler. As the MRP processing area is the only source within each of the Sectors (Fig. 2b), it is reasonable to assume that this increment in the dust signal is the contribution from the MRP.

Table 2 The Normalised Dust Signal and NET Normalised Dust Signal for 3 different processing periods: before substantial skimmer processing; during skimmer processing but before new dust-controls; and, during skimmer processing and after new dust-controls

	Period Description	Before substantial skimmer processing	Skimmer processing & no controls	Skimmer processing & new controls
	Deployment	2	4	7
A	A3 (upwind)	0.52	0.12	0.04
	A4 (upwind)	0.38	0.09	0.03
	A11 (downwind)	0.94	0.75	0.16
C	C3 (downwind)	1.01	1.09	0.19
	C4 (downwind)	1.13	0.65	0.15
	C11 (upwind)	0.46	0.05	0.07
NET	C3–A3	0.49	0.97	0.15
	C4–A4	0.75	0.56	0.12
	A11–C11	0.48	0.7	0.09

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Moreover, the NET Normalised Dust Signal for each Sector remains in the same order of magnitude for the 3 different processing periods, and a substantial reduction can been seen following the introduction of new dust-control measures (Section 3.2.3). Detecting this reduction strongly indicates that the samplers are measuring emissions from the processing area, and that the DPAS system can therefore identify the contribution made by the MRP.

3.2.2. Monitoring the MRP dust levels before and during skimmer processing. Two metrics are used to consider how MRP dust levels varied before and during the processing of the skimmer material. Fig. 4 shows the Total NET Normalised Dust Signal, i.e. the combined net contribution from Sectors C3–A3 and C4–A4 that encompass the central processing area; and the Total Normalised Dust Signal, i.e. the total dust signal recorded in Sectors C1–C4 which encompasses the wider MRP operations and includes service roads and stockpiling areas (Fig. 2b). Neither of the Dust Signal metrics appears to increase following the start of skimmer processing; instead both the Dust Signal metrics reduce with time, particularly once the new dust controls measures were introduced (Section 3.2.3). Moreover, the processing of the skimmer material has no relationship with the number of air quality exceedance days at the nearby monitoring station. The greatest number of exceedances days occurred before the skimmer processing started; and, during the more substantial skimmer processing period (Deployments 3 onwards; Table 1), there were 5 deployments with only 1 or 0 exceedance days.

Fig. 4 The Total NET Normalised Dust Signal, i.e. the total net contribution from the sectors that encompass the central MRP processing area (Sectors C3 & C4; Fig. 2b); and the Total Normalised Dust Signal, i.e. the total dust signal recorded in Sectors C1–C4 which encompasses the wider MRP operations and includes service roads and stockpiling areas (Fig. 2b), for each of the 9 deployments are shown on the primary y axis. The numbers of exceedance days are shown on the secondary y axis. The different periods of skimmer processing, and the introduction of dust-controls are shown at the top of the figure.

3.2.3. Detecting any changes to dust levels following new control measures. As both Table 2 and Fig. 4 show, the different Dust Signal metrics show that the dust levels reduced following the introduction of new dust controls (Section 2.2.1). This reduction is quantified in Fig. 5 which shows the Normalised Dust Signal for Sectors C1–C4, for 3 separate periods: (i) before the dust controls; (ii) a transitional period, so named as the different dust controls were introduced gradually over this approximate 3 month period; and (iii) after the new dust controls were introduced. With the exception of Sector C1, all Normalised Dust Signal values reduced from the initial period to the transitional period; all Normalised Dust Signal values reduced, or remained the same from the transitional period to the final period. Overall, the fugitive dust emissions reduced in Sectors C1–C4 by approximately 50–70%. These results show that the DPAS system can detect the difference in dust levels before and after the new dust control measures.

Fig. 5 The Normalised Dust Signal for Sectors C1–C4, for 3 separate periods: (i) before the dust controls; (ii) a transitional period with some dust controls; and (iii) after the new dust controls were introduced.

3.3. Discussion of results

The DPAS is an unpowered, low-maintenance sampler that can be located in complex industrial environments. Section 3.1 demonstrated that the DPASs can monitor the intended source area and clearly resolve fugitive dust emissions from different sources (e.g.Fig. 3). It can resolve emissions at higher directional resolution than the Standard BS 1747 directional gauge; it does not require a power source like the Directional Frisbee gauge; and, unlike the WSL wedge dust flux gauge and the Directional Frisbee gauge, several samplers are not required to resolve dust from multiple wind sectors. Moreover the dust collected on and within the sampling medium can be measured using the range of methods presented in Section 2.3 (photographic, mass-loading, magnetic), or alternatively the dust could be easily extracted from the foam sampling medium for further chemical analysis. This array of measurement methods are not possible when using the Standard BS 1747, Frisbee, and WSL wedge dust flux gauges, which rely on gravimetric determination,⁴ and are far more complicated when using the DustScan monitoring system for it is difficult to extract the sampled dust from the sticky pad collection medium.⁹ The DPAS therefore represents an alternative monitoring tool and approach for measuring dust, and there is clear further potential for more detailed source attribution and apportionment using either chemical analysis, or more sensitive magnetic testing.

By using mass-loading as the measurement method, this study was able to: (i) identify and measure the contribution made by a processing operation; (ii) monitor this contribution during the processing of skimmer material; and (iii) detect any changes to this contribution following new dust controls. Skimmer is a particularly dusty by-product of the steel-making industry for it arrives at the MRP at a temperature too high for safe water-dampening. Accordingly the Dust Signal from the MRP did increase following the start of substantial skimmer processing (Fig. 4); however, this increased Dust Signal is not linked to exceedances of the daily PM₁₀ standard at the nearby air quality monitoring station, and moreover, it decreased significantly following the introduction of new dust control measures (Fig. 5). The DPAS system therefore provides quantitative evidence on the sources of fugitive emissions, and how they vary with different processing operations and dust control measures. The high-directional resolution clearly and quantitatively indicates the emissions associated with different operators, and therefore different operational permits (Fig. 3). Such information is invaluable for the operator or regulator for it provides a quantitative evidence-base for current or future regulatory decisions. This quantitative evidence is also useful for both emissions inventories and dispersion modelling studies which generally rely on estimates for fugitive dust emissions for no other data exists, e.g.^2,3

However, the DPAS system is not without limitations. The DPAS is a flux sampler, and the current measurement techniques have no mass concentration equivalents. Additionally, the dust sampler does not have a size selective inlet, and samples all sizes of PM, including those with an aerodynamic diameter >10 µm, which are not legislated in current Air Quality Standards and Objectives. These 2 factors prevent the results from the DPAS being directly compared with mean PM₁₀ concentrations recorded at the nearby air quality monitoring site (Fig. 2a), or directly with the Air Quality Standards and Objectives. Previous applications of the DPAS to measure NO₂ have been able to estimate an air concentration by considering Fick's First law of diffusion;¹⁰ this is unsuitable for PM. However, for the future, it may be possible to develop an empirical relationship between the Dust Signal, and PM₁₀ concentrations by co-deploying the DPAS alongside an active air quality monitor such as the Tapered Element Oscillating Micro-balance (TEOM), which is widely used for monitoring air quality in the UK as part of the Automatic Urban and Rural network.

Moreover, as the DPAS is a flux sampler deployed on an approximately monthly timescale, it cannot discriminate individual ‘dust' events, unlike active sampling devices such as TEOMs which can record particle concentrations at hourly, or sub-hourly temporal resolution. It is therefore essential to liaise closely with the MRP operator to understand how dust quantities may have varied over each deployment period. Also, the source attribution undertaken in this study is circumstantial, and uses the high-directional resolution of the sampler to determine the most likely sources of emissions. Again, it is therefore essential to liaise with the MRP operator to understand the processing operations, and how potential sources (such as windrows, mobile conveyors, haulage routes) may have changed location or importance during the deployment period. Future planned chemical analysis and/or more magnetic testing will reduce this uncertainty.

4. Conclusions & forward look

This article has summarised the recent design modifications made to the Directional Passive Air Sampler developed by Lancaster University and the Environment Agency. The DPAS can collect separate samples for winds from different directions (12 × 30° sectors), and the collected dust may be quantified using a variety of measurement methods. The DPAS is therefore unique among directional air samplers, and provides another tool for Air Quality monitoring.

The study has clearly demonstrated the ability of the DPAS to sample industrial dust, and to resolve different emissions sources. Moreover, the DPAS could: (i) identify and measure the contribution made by the MRP; (ii) monitor this contribution during the processing of the dustier skimmer material; and (iii) detect the reduction to this contribution following new dust controls. Results from the DPAS therefore provide a unique evidence-base for current and future operational or regulatory decisions.

Ultimately, whether the DPAS system is adopted on a larger-scale depends on its ability to add cost-effective value to routine monitoring situations. As such, the DPAS will be trialled for other nuisance dust situations that are typically encountered by environmental regulators, for example: waste-transfer stations, metal scrap yards, and/or wood recycling plants. Future studies will therefore: (i) apply the DPAS system in such situations; (ii) investigate alternative measurement methods such as more sensitive magnetic testing, and chemical analysis for more definitive source attribution and apportionment; and, (iii) investigate the commercial potential of the DPAS system for large-scale monitoring use.

Acknowledgements

The authors are grateful to the Environment Agency for funding this research, and to John Dronfield, Charlie Harris, Ross Thomson, for providing this sampling opportunity. We are also very grateful to the operators at the steelworks complex for facilitating and helping with fieldwork, in particular to Simon Greenfield, Fraser Lyth and Stewart Howson. We would also like to thank in no particular order: North Lincolnshire District Council; the Lancaster Product Development Unit and Jenny Roberts of Sprocket Design Consultancy for undertaking the redesign of the DPAS; Dave Lewis in the Lancaster Environment Centre Workshop; Simon Chew for help with photography; and Dr Vassil Karloukovski, and Prof. Barbara Maher in the Centre for Environmental Magnetism and Palaeomagnetism. We would like to thank two anonymous reviewers for their positive and helpful comments.

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