Field and laboratory comparison of PM10 instruments in high winds

Aeolian Research 32 (2018) 42–52

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Field and laboratory comparison of PM10 instruments in high winds a,⁎

Brenton Sharratt , Huawei Pi a b c

T

b,c

USDA-ARS, 215 Johnson Hall, Washington State University, Pullman, WA, USA State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China Washington State University, Pullman, WA, USA

A B S T R A C T Instruments capable of measuring PM10 (particulate matter ≤10 µm in aerodynamic diameter) concentrations may vary in performance as a result of different technologies utilized in measuring PM10. Therefore, the performance of five instruments capable of measuring PM10 concentrations above eroding soil surfaces was tested during high wind events at field sites in the Columbia Plateau and inside a wind tunnel. Comparisons among the Big Spring Number Eight (BSNE) sampler, DustTrak monitor, E-sampler, High-Volume sampler, and Tapered Element Oscillating Microbalance (TEOM) monitor were made at field sites during nine wind erosion events and inside a wind tunnel at two wind speeds (7 and 12 m s−1) and two ambient PM10 concentrations (2 and 50 mg m−3). PM10 concentrations were similar for the High-Volume sampler and TEOM monitor as well as for the BSNE samplers and DustTrak monitors but higher for the High-Volume sampler and TEOM monitor than the E-sampler during field erosion events. Based upon wind tunnel experiments, the TEOM monitor measured the highest PM10 concentration while the DustTrak monitor typically measured the lowest PM10 concentration as compared with other instruments. In addition, PM10 concentration appeared to lower for all instruments at a wind speed of 12 as compared with 7 m s−1 inside the wind tunnel. Differences in the performance of instruments in measuring PM10 concentration poses risks in comparing PM10 concentration among different instrument types or using multiple instrument types to jointly measure concentrations in the field or laboratory or even the same instrument type subject to different wind speeds.

1. Introduction Air quality within the Columbia Plateau region of the Pacific Northwest United States is impacted by the emission of fine particulates from agricultural soils during high wind events. In fact, windblown dust emanating from agricultural lands in the region has been the cause of vehicular accidents due to poor visibility (Hudson and Cary, 1999) and exceedance of the US Environmental Protection Agency (USEPA) ambient air quality standard for PM10 (Sharratt and Edgar, 2011; Sharratt and Lauer, 2006). Emission of PM10 from agricultural land is caused by the exposure of erodible soil to high winds, particularly during the fallow phase of traditional winter wheat – summer fallow rotations (Schillinger and Young, 2004). The soil is subject to multiple tillage operations during the fallow phase of the rotation; these tillage operations degrade soil aggregates and bury crop residue that would otherwise protect the soil surface from wind erosion. PM10 concentrations have been monitored above eroding agricultural soils in the Columbia Plateau. Kjelgaard et al. (2004) measured PM 10 concentrations during erosion events using a High-Volume

⁎

sampler and TEOM monitor and found good agreement between the two instruments. Sharratt et al. (2007) noted a steep PM10 concentration gradient from the soil surface to a height of several meters above eroding surfaces. Based upon collocated instruments, they found the BSNE sampler underestimated or High-Volume sampler overestimated PM10 concentration by 150%. Sharratt and Feng (2009) used the High-Volume sampler as the reference or basis of comparison when using the BSNE sampler and E-sampler to ascertain PM10 concentration profiles in the field during high wind events. The BSNE sampler, however, was designed to trap saltation-size and suspension-size particles for assessing horizontal sediment flux (Fryrear, 1986) and not PM10. Sharratt et al. (2010), Feng et al. (2011) and Houser and Nickling (2001) have used DustTrak monitors to assess PM10 or PM2.5 concentrations above eroding soil inside a wind tunnel. Variations in the performance of PM10 instruments are expected based upon different technologies utilized in measuring PM10. Filterbased technologies, like that employed by the High-Volume sampler, determine PM10 concentration from the mass of PM10 within an air sample. PM10 is extracted from the air sample using cyclones and

Corresponding author at: USDA-ARS, 215 Johnson Hall, Washington State University, Pullman, WA 99164, USA. E-mail address: [email protected] (B. Sharratt).

https://doi.org/10.1016/j.aeolia.2018.01.006 Received 1 December 2017; Received in revised form 19 January 2018; Accepted 23 January 2018 Available online 05 February 2018 1875-9637/ Published by Elsevier B.V.

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filters, and weighed. Light-scattering technologies, like that employed in the DustTrak monitor, use light scattering to ascertain PM10 concentration in an air sample. This technology also requires cyclones to aid in separating PM10 in an air sample. Passive instruments, such as the BSNE sampler, can also be used to assess PM10 concentrations, but the use of this instrument requires knowledge of the instrument efficiency in trapping PM10 and the volume of air moving through the instrument. Cambra-Lopez et al. (2015) found light-scattering devices (i.e., DustTrak monitor) underestimated PM10 concentrations as compared with low volume filter-based samplers in pig and poultry houses, but not turkey houses. They attributed differences in instrument performance to differences in particle characteristics between pig/poultry and turkey houses. Differences in performance between light-scattering and filter-based instruments may also be amplified at higher PM10 concentrations. This may in part be attributed to the greater sensitivity of light-scattering instruments at lower concentrations (Vincent, 2007). In contrast, Lehocky and Williams (1996) found no difference in respirable dust measured by a DustTrak and filter-based sampler within an electric power facility. Vega et al. (2003) compared ambient PM10 concentrations in industrial and residential areas of Mexico City as measured by a high-volume filter-based sampler and TEOM monitor. They concluded that the TEOM monitor overestimated PM10 concentrations. Watson et al. (2011) examined the performance of eight PM10 samplers in measuring fugitive dust concentrations downwind from sand and gravel operations in California. Among the samplers tested were a High-Volume sampler, DustTrak monitor, and E-sampler. They found good agreement between the DustTrak monitor and Esampler, but both instruments underestimated PM10 concentrations as compared with the High-Volume sampler. Few if any studies have been undertaken to compare PM10 instrument performance under both laboratory and field conditions. The intent of this paper was to compare the performance of PM10 instruments that have been used in field and laboratory experiments in the Columbia Plateau to ascertain PM10 concentrations above eroding soil surfaces. These instruments include the BSNE sampler, DustTrak monitor, E-sampler, High-Volume sampler, and TEOM monitor. This information will provide scientists with an understanding of inherent differences in the performance of PM10 instruments used in laboratory and field environments and especially in measuring windblown dust.

efficiency of the sampler in trapping PM10, and volume of air passing through the sampler (Goossens and Buck, 2012; Sharratt et al., 2007). Of the five instruments used in this study, only the High-Volume sampler and TEOM monitor are approved by the USEPA in measuring ambient PM10 concentrations (USEPA, 2016). The High-Volume sampler is designated as a federal reference method and as such is the basis for comparison of other instruments in measuring PM10 concentration in this study. The TEOM monitor is designated as an equivalence method and therefore should be comparable to federal reference methods under co-located field conditions (USEPA, 2002). These five instruments have been used in field and laboratory studies to assess PM10 concentrations above eroding agricultural soils (Sharratt et al., 2007; Kjelgaard et al., 2004; Houser and Nickling, 2001). 2.1. Agricultural fields We compared the performance of PM10 instruments during wind erosion events at agricultural field sites in 2007–2009. The field site in 2007 and 2008 was located at the USDA Palouse Conservation Field Station in Pullman, WA (46°45′33″N, 117°12′01″W, elevation of 755 m) while the field site in 2009 was located near Washtucna, WA (46°53′06″N, 118°17′36″W, elevation of 485 m). Annual precipitation at the Palouse Conservation Field Station is 535 mm and at the field site near Washtucna is 270 mm. The field site in 2007 and 2008 had inclusions of Latah silt loam (fine, mixed, superactive, mesic Xeric Argialbolls) and Palouse silt loam (fine-silty, mixed, superactive, mesic Pachic Ultic Haploxerolls) while the field site in 2009 had inclusions of Esquatzel fine sandy loam (coarse-silty, mixed, superactive, mesic Torrifluventic Haploxerolls) and Farrell fine sandy loam (coarse-loamy, mixed, superactive, mesic Calcidic Haploxerolls). Dispersed particle size analysis indicated the soil at the field site in 2007 and 2008 had 14% clay, 68% silt, 18% sand, 33% PM10, and a mean particle diameter of 0.020 mm while the soil at the field site in 2009 had 8% clay, 52% silt, 40% sand, 21% PM10, and a mean particle diameter of 0.041 mm. The field site in 2007 and 2008 was 90 × 140 m and the field site in 2009 was 800 × 1600 m. The long axis of the field site in 2007 and 2008 and the short axis of the field site in 2009 was oriented east–west and in the direction of the prevailing winds (Fig. 1). Wheat and barley was grown on land to the south and west of the field site in respectively 2007 and 2008 while wheat was grown on land to the south and west of the field site in 2009. Thus, wheat or barley grown on adjacent land provided a nonerodible boundary on the windward side of our field sites (Fig. 1). The field site in 2007 was in spring pea the previous year and was cultivated in November 2006 and April 2007. The field was rodweeded on 1 June 2007 after which instruments were installed on the leeward side of the field (Fig. 1). The field was again rodweeded on 10 July and 21 July 2007. No rain occurred between the time of rodweeding and observed wind erosion events on 13 July and 3 and 7 August 2007. The field site was maintained in fallow for the duration of 2007 and cultivated on 29 April and 2 June 2008. The field was rodweeded on 17 June 2008 after which instruments were installed on the leeward side of the field. The field was again rodweeded on 31 July 2008. Rain (4 mm) occurred between the June rodweeding and first erosion event on 10 July 2008, but no precipitation occurred between the July rodweeding and last two erosion events on 1 and 18 August 2008. At the time of the observed erosion events in 2008, surface residue cover was 5% and biomass was 75 kg ha−1. In addition, the aggregate geometric mean diameter was 1.5 mm and erodible fraction (< 0.84 mm size fraction) was 0.51 based upon the aggregate size distribution of soil collected from the 0–3 cm depth. The field site in 2009 was in winter wheat the previous year and subsequently maintained in fallow by cultivating in October 2008 and April 2009. Instruments were installed on the leeward side of the field on 28 May 2009. The field was rodweeded on 15 June and 15 July 2009 and then sown to winter wheat on 1 September 2009. No rain occurred between the time of rodweeding or sowing and

2. Materials and methods This study compared PM10 concentrations measured by five instruments above eroding soil surfaces in agricultural fields and inside a wind tunnel. The five instruments included a DustTrak monitor (TSI, Inc. Model 8420, St. Paul, MN), High-Volume sampler (GrasebyAnderson Model 1200), TEOM monitor (Thermo Scientific Model 1400a, Franklin, MD), E-sampler (Met One Instruments, Inc., Grants Pass, OR), and BSNE sampler (Custom Products and Consulting, Big Spring, TX). The DustTrak monitor was equipped with a 10 µm inlet conditioner and uses light scattering to measure PM10 concentration over a range of 0.001–100 mg m−3; flow rate was set to 1.7 l min−1. The E-sampler was equipped with a 10 µm cyclone and uses light scattering and a gravimetric filter system to measure PM10 concentration over a range of 0.001–65 mg m−3; flow rate was set to 2 l min−1. The High-Volume sampler was equipped with a PM10 selective-size inlet and uses a gravimetric filter system to measure PM10 concentration; a flow rate of 1.13 l min−1 was maintained using a mass flow controller. The TEOM monitor was equipped with a PM10 inlet and uses a gravimetric filter system and mass transducer to measure PM10 concentration; flow rate through the PM10 inlet was set to 16.7 l min−1. The BSNE was designed to measure horizontal sediment flux by trapping suspended soil particles passing through a 0.001 m2 opening that is oriented into the wind. PM10 concentration was estimated using the BSNE sampler by measuring the mass of sediment trapped by the sampler, PM10 fraction of the trapped sediment, 43

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instruments under prevailing winds during an erosion event. A meteorological tower was installed along the planar surface to measure air temperature and relative humidity at a height of 2 m; wind speed at a height of 1, 2, and 3 m; wind direction at a height of 3 m; and precipitation. Meteorological data were recorded by a datalogger. Within hours (< 12) of an anticipated wind erosion event, preweighed gravimetric filters were installed in the E-samplers and HighVolume samplers. Gravimetric filters were installed in the E-samplers to obtain the “k factor” required for accurately determining aerosol concentration. Inlets were cleaned, internal filters checked for discoloration, and flow rates checked for accuracy on the DustTrak monitors and E-samplers. In addition, the E-samplers were leak tested and DustTrak monitors were calibrated at 0 mg m−3. Manufacturer recommended maintenance was performed on the High-Volume samplers and TEOM monitors (i.e., leak test, clean inlet, and calibration of flow controller) prior to deployment in the field each year. All instruments were initialized at the same time prior to the anticipated event by turning on DustTrak monitors, E-samplers, High-Volume samplers, TEOM monitors, and datalogger and cleaning BSNE samplers. PM10 concentration was recorded every 5 min from DustTrak monitors, E-samplers, and TEOM monitors and on an event basis from BSNE and High-Volume samplers. Meteorological data were also recorded every 5 min. All data were retrieved from the field site the following day. Sediment collected by each BSNE sampler during an event was processed through a sonic sieve (Advantech Manufacturing Inc., New Berlin, WI), equipped with sieves having 0.010, 0.038 and 0.063 mm openings, to determine the PM10 fraction of the sediment. PM10 fraction obtained with the sonic sieve may overestimate the true PM10 fraction of the sediment due to breakage of aggregates during the sieving process. Nevertheless, BSNE PM10 concentration was then determined according to:

C= Fig. 1. Diagram of field sites used to test the performance of the BSNE sampler, DustTrak monitor, E-sampler, High-Volume sampler, and TEOM monitor in measuring PM10 concentration above eroding surfaces. Instruments were installed at heights of 1–3 m above the surface and on the leeward side of the field. A meterological tower (met) was co-located with the instruments.

M uStf

(1)

where C is the BSNE PM10 concentration (mg m−3), M is the PM10 mass in the BSNE sampler (mg), u is wind speed (m s−1), S is the area of BSNE opening (m2), t is the time the sampler was deployed in the field during an erosion event (s), and f is the PM10 catch efficiency of BSNE samplers. The BSNE catch efficiency for PM10 varied as a function of wind speed as determined by Sharratt and Feng (2009).

observed erosion events on 24 June and 3 and 5 September 2009. At the time of the erosion events in 2009, surface residue cover was 5% while the aggregate geometric mean diameter was 2.1 mm and erodible fraction was 0.79. In 2007 and 2008, BSNE samplers, DustTrak monitors, E-samplers, and High-Volume samplers were installed at heights of 1, 2 and 3 m while TEOM monitors were installed at heights of 2 and 3 m above the soil surface. In 2009, all instruments were installed at 1 and 2 m above the soil surface. BSNE samplers, DustTrak monitors, and E-samplers were mounted on poles which were anchored into the soil. The DustTrak monitors and E-samplers were equipped with omni-directional inlets while BSNE samplers were equipped with wind vanes to orient the instrument in the direction of the wind. The instruments were placed along an N-S planar surface on the leeward side of the field to maintain the same fetch to the windward (west) side of the field (Fig. 1). Based upon field dimensions, placement of instruments in the field, and wind direction, fetch varied from 75 to 1100 m across erosion events (see results). A duplicate set of BSNE samplers was installed at the field site in 2007 and 2008. In 2009, quadruple sets of BSNE samplers and duplicate sets of DustTrak monitors and E-samplers were installed in the field. Duplicate sets of instruments were separated some distance apart and dispersed among other instrumentation along the planar surface. As recommended by the manufacturer, High-Volume samplers were separated from any other instrument by a distance of at least 2 m. Instruments were spaced across a distance of 15 and 30 m along the planar surface at the respective 2007/2008 and 2009 field sites such as to avoid any obstruction in airflow between adjacent

2.2. Wind tunnel The performance of the five PM10 instruments was assessed inside a wind tunnel (Fig. 2). The wind tunnel, described by Pietersma et al. (1996) and Sharratt (2007), was located inside a non-regulated climate facility at the USDA Palouse Conservation Field Station near Pullman, WA. The facility had a 12 × 30 m concrete floor and was enclosed on all

Fig. 2. Top view of the portable wind tunnel showing the location of the sampling zone within the working section. Instruments were compared in measuring PM10 concentration within the sampling zone. A fixed-position pitot tube and DustTrak monitor were located between either wall of the tunnel and the sampling zone.

44

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within the manufacturer’s specified range for the DustTrak (100 mg m−3), E-sampler (65 mg m−3), and TEOM (5000 mg m−3). The BSNE and High-Volume samplers have an unspecified concentration range. We examined uniformity in wind speed and PM10 concentration in the sampling zone of the working section of the wind tunnel by installing fans and baffles at the entrance of the working section, repositioning the fertilizer box, adjusting the height of the drop tubes to the fertilizer box, and modifying the grid assembly. As a result of removing vertical bars from the grid assembly and adjusting the height of drop tubes to 0.95 m above the floor, the variation in wind speed and PM10 concentration within the sampling zone was near 10% as required by the US EPA (Heist et al., 2001; USEPA, 2016). One fixed-position pitot tube and inlet to a DustTrak monitor were installed 0.28 m from each of the two walls and 0.7 m above the floor and 6.5 m downwind from the grid assembly in the working section (adjacent to the sampling zone) of the wind tunnel. These fixed-position instruments remained in place for the duration of the experiment for the purpose of monitoring the temporal variability in wind speed and PM10 concentration across all experimental runs. The wind tunnel could not accommodate the simultaneous placement of the five PM10 instruments inside the sampling zone. Due to the confined width of the sampling zone, the BSNE sampler, DustTrak monitor, and E-sampler were tested simultaneously together while the High-Volume sampler and TEOM monitor were tested separately from all other instruments. The performance of all instruments was examined at target PM10 concentrations of 2 and 50 mg m−3 during sustained winds of 7 and 12 m s−1. Instruments were monitored for 20 min at each PM10 concentration and wind speed combination. The experimental design was a split-split plot with four replications with PM10 concentration as the main plot treatment, wind speed as the subplot treatment, and instruments as the sub-subplot treatment. To minimize the effect of position in the sampling zone on performance of the BSNE sampler, DustTrak monitor, and E-sampler, these instruments were each tested at three positions (0.5, 0.75 and 1.0 m from either wall) at a height of 0.7 m above the floor of the tunnel. The inlet of these instruments was centered at the respective three positions. In contrast, the inlet of the High-Volume sampler and TEOM monitor was positioned equidistant from either wall at a height of 0.7 m above the floor of the tunnel. One replication constituted testing the BSNE sampler, DustTrak monitor, and E-sampler at the three different positions in the wind tunnel. To avoid instrument bias, duplicates of each type of PM10 instrument were also tested during this study. Pitot tube differential pressure transmitters were calibrated twice daily using a manometer. DustTrak monitors and E-samplers were factory calibrated prior to the experiment. Once each day, E-samplers were calibrated for temperature and pressure and checked for leaks and proper flow rate while DustTrak monitors were checked for proper flow rate and zero calibration. DustTrak and E-sampler inlets were cleaned after every test at the high PM10 concentration while internal or sheath filters were checked after every third test at the high PM10 concentration and changed when discolored. Manufacturer recommended maintenance was performed on the High-Volume samplers and TEOM monitors (i.e., leak test, clean inlet, and calibration of flow controller) prior to the experiment. E-sampler and High-Volume sampler gravimetric filters were weighed before and after every 20 min test; E-sampler gravimetric filters were used to obtain the instrument k factor. Sediment collected by the BSNE sampler during each test was processed through a sonic sieve apparatus to determine the PM10 mass in the BSNE sampler. PM10 concentration was then ascertained according to Eq. (1) where the BSNE catch efficiency for PM10 varied as a function of wind speed for Ritzville silt loam (Sharratt and Feng, 2009). PM10 concentration was recorded every 1 s from DustTrak monitors and E-samplers, 2 s from TEOM monitors, and on an event basis (each test) from BSNE and High-Volume samplers. Wind tunnel tests were only performed on days when relative humidity was below 65% because liquid bridges formed at higher

sides with 4 × 3 m doors at both ends. The non-circulating wind tunnel was set up inside the facility with the end of the tunnel protruding through the door of the facility. Winds inside the tunnel were generated by a 1.4-m diameter fan and then conditioned by a diffuser and honeycomb-screen prior to passing through a grid assembly to facilitate turbulent flow in the working section. A fertilizer box, with 19 mm diameter drop tubes, installed above the grid assembly facilitated injecting dust into the air stream 0.95 m above the floor of the wind tunnel. An electronic control on the box allowed the dust to be injected into the air stream at a known rate to achieved the desired PM10 concentration. The source of dust injected into the air stream of the wind tunnel was soil obtained from the Washington State University Dryland Research Station located near Lind, WA (47°00′ N, 118°34′W; elevation 515 m). The soil type was a Ritzville silt loam (Andic Aridic Haplustoll). Dispersed particle size analysis (measured using a Malvern Mastersizer S laser diffractometer) indicated the soil was comprised of 13% clay, 60% silt, 27% sand, and 25% PM10 and had a mean particle diameter of 0.024 mm. The soil collected at the station was sieved through a 2-mm screen to obtain only the erodible fraction of the soil which was injected into the airstream of the wind tunnel. Aggregate size distribution of the erodible soil size fraction was assessed by sieving the soil through 0.84, 0.5, 0.15, 0.105, 0.063, 0.038, and 0.010 mm screens. The two largest size fractions were obtained by hand sieving while the remaining size fractions were obtained using a sonic sieve apparatus. The erodible soil fraction had an aggregate geometric mean diameter of 0.066 mm and 2.3% PM10. The erodible fraction was stored in an oven (30 °C) prior to use and was removed from the fertilizer box after the last wind tunnel test each day. Soil water content was assessed during the first, middle, and last test each day. Soil water content varied from 0.99 to 1.52% during this experiment, which was below that required (4%) to enhance binding of soil particles and effect a change in threshold velocity of Ritzville silt loam (Sharratt et al., 2013). The working section of the tunnel was redesigned to mimic the horizontal and vertical dimensions of the USEPA wind tunnel in testing air quality instruments (Ranade et al., 1991; Heist et al., 2001). The redesigned working section was 1.5 m wide, 1.2 m tall, and 7.8 m long. All PM10 instruments were tested inside a “sampling zone” of the working section where uniformity was assured in wind speed and PM10 concentration. Uniformity in wind speed and PM10 concentration in the sampling zone is a major criteria for designing wind tunnels in testing instruments (Cheng et al., 2004; USEPA, 2016). The sampling zone was an imaginary suspended vertical plane located 6.5 m downwind of the grid assembly in the working section. The sampling zone was located 0.55–0.85 m above the concrete floor and 0.3–1.2 m from either wall of the tunnel. Uniformity in wind speed and PM10 concentration inside the sampling zone was examined by measuring wind speed and PM10 concentration at 24 positions (eight horizontal positions at 0.55, 0.70, and 0.85 m above the floor) within the zone. Wind speed and PM10 were measured at each position for five minutes using respectively a pitot tube and DustTrak monitor. The pitot tube was monitored at a frequency of 0.1 s and wind speed recorded every 1 s using a datalogger. The sampling inlet of the DustTrak was constructed of stainless steel having a diameter (2 or 2.5 mm) necessary to achieve isokinetic conditions across the face of the inlet. The DustTrak recorded PM10 concentration every 1 s. Uniformity of wind speed and PM10 concentration was examined at a target wind speed of 7 and 12 m s−1 and PM10 concentration of 2 and 50 mg m−3. The lower wind speed is near the threshold required to initiate erosion in the Columbia Plateau (Saxton et al., 2000) and closely conforms to US EPA testing criteria (USEPA, 2016). The higher wind speed is the range that can be attained inside the wind tunnel and recurs annually in the Columbia Plateau (Wentz and Sinclair, 1981). PM10 concentrations of 2 and 50 mg m−3 are representative of concentrations at heights of respectively 1 and < 0.1 m above the soil surface during extreme wind erosion events in the Columbia Plateau (Sharratt et al., 2007). The higher concentration is 45

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humidity can enhance aggregation or binding of soil particles (Ravi et al., 2006). Air temperature and relative humidity were monitored every 1 min during the experiment using a datalogger. Air temperature and relative humidity varied from 16 to 35 °C and 19 and 55% during this experiment.

Table 1 Characteristics of high wind events during which PM10 concentration was measured by five instruments at agricultural field sites in eastern Washington during 2007–2009.

2.3. Statistical analyses Differences in PM10 concentrations obtained with the five instruments during wind erosion events at agricultural field sites in 2007, 2008, and 2009 were examined using a split-plot Analysis of Variance or ANOVA (CoStat version 6.204, CoHort Software, Monterey, CA). Years were treated as the main plot factor, instruments as the subplot factor, and events within a year as blocks in the split-plot experimental design. PM10 concentrations were log transformed prior to performing ANOVA as variances were heterogeneous. Comparisons between the duplicate set of BSNE samplers mounted at the same height in 2007 and 2008 were performed using a paired-samples t-test. Comparisons among quadruple sets of BSNE samplers and between duplicate sets of DustTrak monitors and E-samplers mounted at the same height in 2009 were performed using respectively an ANOVA and paired-samples ttest. Differences in PM10 measurements obtained with the five instruments in the wind tunnel were examined using Analysis of Covariance or ANOCVA (CoStat version 6.204) in a split-split plot experimental design. PM10 concentration was the main plot factor, wind speed the subplot factor, and instrument the sub-subplot factor. PM10 concentration measured by the fixed-position DustTrak monitors was used as the co-variate in the ANOCVA. Prior to performing an ANOCVA, PM10 concentrations were log transformed due to heterogeneous variances. In addition, we examined the assumption of homogeneity of regression slopes of the relationship between instrument PM10 concentration and the covariate. Regression slopes were homogenous as indicated by the lack of significant interaction between PM10 concentration and the covariate.

Year1

Date of event

Duration (min)2

Maximum wind speed (m s−1)

Wind direction3

Horizontal sediment flux (kg m−2)4

2007

13 July 3 August 7 August 10 July 1 August 18 August 24 June 3 September 5 September

210 275 330 535 390 325 540 415 165

10.2 8.7 10.8 13.2 10.6 16.7 11.8 13.8 13.3

247 251 254 249 247 255 237 231 315

0.10 0.09 0.04 0.31 0.22 2.04 0.06 7.62 3.47

2008

2009

1 The field site in 2007 and 2008 was located in Pullman, WA while the site in 2009 was located near Washtucna, WA. 2 Number of minutes wind speed at 3 m exceeded 6.4 m s−1. 3 Mean direction when wind speed exceeded 6.4 m−1. 4 Horizontal sediment flux determined from mass of sediment trapped by BSNE sampler at 1-m height.

3.1.1. Variation in fetch across erosion events Although winds were predominately from the WSW across most erosion events (Table 1), subtle changes in wind direction influenced fetch. The fetch, or distance upwind of the instruments that was maintained in fallow and susceptible to wind erosion, varied across the N-S planar surface over which the instruments were installed as well as across erosion events at each field site. In 2007, for example, winds were from 247° during the 13 July erosion event, 251° during the 3 August event, and 254° during the 7 August event. For these respective erosion events, the fetch varied from 75 to 115, 90–140, and 110–165 m for instruments installed across the N-S planar surface. Similarly, in 2008, winds were from 249° during the 10 July erosion event, 247° during the 1 August event, and 255° during the 18 August event. For the 10 July and 1 August events, the fetch varied from 80 to 120 m while the fetch for the 18 August event varied from 115 to 175 m for instruments across the planar surface. Likewise, in 2009, winds were from 237, 231, and 315° during the respective 24 June, 3 September, and 5 September erosion events. Based upon these wind directions, instruments across the N-S planar surface had a fetch that varied from 120 to 200 m for the 24 June erosion event, 105–175 m for the 3 September event, and 1100 m for the 5 September event. Differences in fetch may influence PM10 concentration across the N-S planar surface, thus producing some uncertainty in the uniformity in PM10 concentrations to which all instruments at a known height were exposed during erosion events. Streamers or ribbons within an eroding field (Baas and Sherman, 2005) may also impact PM10 concentrations across the instrumented planar surface.

3. Results and discussion Previous studies have found discrepancies in PM10 concentration measured by co-located instruments (Chung et al., 2001; Sharratt et al., 2007; Wanjura et al., 2008). In fact, discrepancies have been noted even between federal reference and equivalence methods in measuring PM10 concentration even though concentrations should be similar between methods (Ono et al., 2000). Although a wide range of instruments have been used in monitoring PM10 concentration above eroding soils, we are not aware of previous studies that compared the performance of this range of instruments. Therefore, comparisons among instruments in measuring PM10 concentration were made during wind erosion events at agricultural field sites from 2007 to 2009 as well as under controlled environmental conditions using a wind tunnel in 2011.

3.1.2. Differences in PM10 concentration between duplicate instruments In 2007 and 2008, duplicate BSNE samplers were installed at a height of 1, 2, and 3 m along the N-S planar surface. These duplicate set of samplers were located 5 m apart. In 2009, four sets of BSNE samplers and two sets of DustTrak monitors and E-samplers were installed at a height of 1 and 2 m along the N-S planar surface. Sets of BSNE samplers were located at most 30 m apart while sets of DustTrak monitors and Esamplers were located 3 m apart. We tested for differences in PM10 concentration measured by duplicate instruments using a paired-sample t-test (data not shown). Based upon nine pairs of data (i.e., one pair of data for each erosion event) for each instrument, no differences in PM10 concentration were found between any pair of BSNE samplers, DustTrak monitors, or E-samplers that were installed at the same height but located at different positions along the planar surface. Thus, similarity in PM10 concentration measured by the same type of instrument, but located some distance apart, would suggest that differences in fetch between the instruments did not influence PM10 concentration across

3.1. Agricultural field sites Characteristics of high wind events during which PM10 concentration was measured by five instruments at the field sites are noted in Table 1. For the purpose of this study, high wind events were characterized by winds sufficient to cause wind erosion. For soils in the Columbia Plateau, wind erosion is initiated at wind speeds of 6.4 m s−1 at a height 3 m (Saxton et al., 2000). Maximum wind speeds at a height of 3 m during the nine erosion events varied from 8.7 to 16.7 m s−1 while the duration of high wind events varied from about 4–9 h. Maximum wind speeds were similar to those reported by Sharratt et al. (2007) during wind erosion events in the Columbia Plateau. Although Sharratt et al. (2007) reported high wind events lasting much longer (14–47 h) than those events listed in Table 1, data were retrieved from our field sites the day after the anticipated erosion event. 46

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Table 2 Average PM10 concentration measured during high wind events at a height of 2 m above eroding agriculture fields in eastern Washington during 2007–2009. Year1

2007

2008

2009

Date of event

13 July 3 August 7 August 10 July 1 August 18 August 24 June 3 September 5 September

PM10 concentration (µg m−3) BSNE

High-Volume

DustTrak

TEOM

E-sampler

44.0 52.9 17.4 107.5 81.8 548.2 15.9 2328.3 933.4

115.2 112.0 72.3 126.5 111.3 523.8 71.5 2482.8 964.2

70.3 35.0 48.6 No data 47.0 332.0 26.1 712.5 285.0

103.3 64.3 101.6 117.6 112.8 559.8 82.1 1624.8 811.1

36.6 17.2 21.6 21.8 40.3 162.7 32.7 711.6 331.4

1 The field site in 2007 and 2008 was located in Pullman, WA while the site in 2009 was located near Washtucna, WA.

E are directly proportional to differences in PM10 concentration gradients as measured by the five types of instruments. PM10 concentration gradients were determined for the duration of the 3 August 2007 event and found to be −8.4 µg m−3 m−1 for the BSNE sampler, −24.8 µg m−3 m−1 for the DustTrak monitor, −15.3 µg m−3 m−1 for the E-sampler, −34.5 µg m−3 m−1 for the High-Volume sampler, and −26.0 µg m−3 m−1 for the TEOM monitor. Thus, E differed by 5–310% among the five types of instruments. PM10 concentrations measured by the five instruments at a height of 2 m above the eroding surface during the nine erosion events observed over the three years (2007–2009) are reported in Table 2. PM10 concentration was highest for the 3 Sep 2009 erosion event when concentrations exceeded 2300 µg m−3 for the BSNE and High-Volume samplers and were 1625 µg m−3 for the TEOM monitor and about 712 µg m−3 for the DustTrak monitor and E-sampler. Interestingly, the lowest PM10 concentration measured by the five instruments occurred on different dates. For example, the lowest PM10 concentration measured by the BSNE sampler, DustTrak monitor, and High-Volume sampler was observed for the 24 Jun 2009 event while the lowest concentration measured by the E-sampler and TEOM monitor was observed for the 3 August 2007 event. While horizontal sediment flux was highest for the 3 Sep 2009 event, sediment flux was lowest for the 7 August event. We expected the highest and lowest PM10 concentration measured by the BSNE sampler to coincide with these respective dates since PM10 concentration as measured by the BSNE sampler is in part dependent upon mass of sediment trapped by the BSNE sampler. However, PM10 concentration as measured by the BSNE sampler is also dependent on wind speed and duration of the erosion event. Therefore, the longer duration of the erosion event on 24 Jun 2009 as compared with 7 Aug 2007 resulted in the lowest PM10 concentration measured by the BSNE sampler. PM10 concentration measured at heights of 1 and 3 m above the eroding surface were respectively higher and lower than concentrations measured at a 2-m height. For example, averaged across erosion events, PM10 concentration at a height of 1 m was 134, 145, 116, 45, and 61% higher than PM0 concentration at a height of 2 m as measured by respectively the BSNE sampler, DustTrak monitor, Esampler, High-Volume sampler, and TEOM monitor. In contrast, PM10 concentration at a height of 3 m was 51, 23, 51, 11, and 27% lower than PM0 concentration at a height of 2 m as measured by respectively the BSNE sampler, DustTrak monitor, E-sampler, High-Volume sampler, and TEOM monitor across erosion events.

Fig. 3. Time series in PM10 concentrations measured by the DustTrak monitor, E-sampler, and TEOM monitor at a height of 2 m above an eroding agricultural field during the 3 August 2007 erosion event.

the N-S planar surface. 3.1.3. Sediment flux and PM10 concentration during erosion events Horizontal sediment flux, as measured by the BSNE samplers, varied across the nine erosion events (Table 1). Horizontal sediment flux was highest for the 3 September 2009 event and lowest for the 7 August 2007 event. Horizontal sediment flux at 1 and 2 m height above the soil surface was respectively 7.62 and 3.50 kg m−2 for the 3 September event and 0.04 and 0.03 kg m−2 for the 7 August event. Sediment flux for the 3 September event was comparable to that previously observed during significant erosion events from agricultural fields in the Columbia Plateau (Sharratt et al., 2007). PM10 concentrations widely fluctuated during erosion events. This is illustrated in Fig. 3 which portrays the time series in PM10 concentrations measured by the DustTrak monitor, E-sampler, and TEOM monitor during the 3 August 2007 erosion event. PM10 concentrations appeared to be elevated for about 4 h and, at a height of 2 m, reached 928 µg m−3 for the DustTrak monitor, 256 µg m−3 for the E-sampler, and 1446 µg m−3 for the TEOM monitor (Fig. 3). During this erosion event, PM10 concentrations at a height of 1 m reached 1259 µg m−3 for the DustTrak monitor and 609 µg m−3 for the E-sampler while PM10 concentrations at a height of 3 m reached 358 µg m−3 for the DustTrak monitor, 75 µg m−3 for the E-sampler, and 638 µg m−3 for the TEOM monitor. These peak concentrations occurred at the same time (1300 h) and, based upon these peak concentrations, the PM10 concentration gradient was −450 µg m−3 m−1 for the DustTrak monitor, −267 µg m−3 m−1 for the E-sampler, and −808 µg m−3 m−1 for the TEOM monitor. Differences in the PM10 concentration gradient, as measured by the five types of instruments, will impact estimates of PM10 emissions during an event. PM10 emission flux is dependent on the eddy diffusivity for PM and PM10 concentration gradient (Prueger and Kustas, 2005) according to:

E = −K

δC δz

3.1.4. PM10 comparison among instruments PM10 concentration measured at a height of 2 m was consistently higher for the High-Volume sampler and TEOM monitor than the DustTrak monitor and E-sampler across all erosion events (Table 2). Similarly, PM10 concentration at a height of 1 m was higher for the High-Volume sampler than the DustTrak monitor and E-sampler across

(2)

where E is emission flux (µg m−2 s−1), K is eddy diffusivity (m2 s−1), C is concentration (µg m−3) and z is height (z). Since K is only dependent on wind speed and atmospheric stability during an event, differences in 47

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TEOM monitors did not systematically decrease with height. As previously indicated, PM10 concentration decreased with height above the eroding surface. In contrast, wind speed increased with height. Averaged across the nine erosion events, wind speed at heights of 1, 2, and 3 m was respectively 3.5, 3.9, and 4.3 m s−1. Thus, the performance of some instruments may be dependent on PM10 concentration and/or wind speed. Sharratt et al. (2007) and Mendez et al. (2016) reported that the efficiency of the BSNE sampler in trapping PM10 varies as a function of wind speed, thus corrections for wind speed must be made when using the BSNE sampler to estimate PM10 concentration. Goossens and Buck (2012) found good agreement between the BSNE sampler and DustTrak monitor in measuring PM10 concentration, but only within a narrow range of wind speed (2–7 m s−1).

Table 3 Differences in PM10 concentration attributed to sources of variation in a split-plot analysis of variance (ANOVA). An ANOVA was used to assess the performance of five instruments in measuring PM10 concentration at heights of 1, 2, and 3 m above agricultural fields during three wind erosion events in each of three years in eastern Washington. Source of variation

Variable

PM10 concentration (µg m−3) at height1 1m

Instrument

Year

BSNE DustTrak E-sampler High-Volume TEOM 2007 2008 2009

2

224 bc 181 bc 127c 297 ab 660 a 69c 199b 561 a

2m

3m

123b 100b 63c 219 a 200 a 51c 134b 312 a

47b 48b 21c 114 a 90 a 33b 92 a No Data

3.2. Wind tunnel

1

Values represent logarithmic transformed data. Means followed by different letters within a column for a given source of variation are significantly different at P ≤ 0.05.

A wind tunnel was used to assess the performance of instruments in measuring PM10 concentration above eroding surfaces because performance may vary with wind speed and/or PM10 concentration. Therefore, performance of instruments in measuring PM10 concentration was compared at two wind speeds and PM10 concentrations.

2

all events. PM10 concentration was also constantly higher for the TEOM monitor than the DustTrak monitor and E-sampler, but lower for the TEOM monitor than the High-Volume sampler, based upon data collected during the three erosion events in 2009. In addition, PM10 concentration at a height of 3 m was consistently higher for the HighVolume sampler than the BSNE sampler, DustTrak monitor, and Esampler and higher for the TEOM monitor than the DustTrak monitor and E-sampler across erosion events in 2007 and 2008. Consistent differences among specific instruments in measuring PM10 concentration across erosion events was substantiated by ANOVA (Table 3). Indeed, the ANOVA indicated significant differences (P ≤ 0.05) among instruments during erosion events. At heights of 1, 2, and 3 m, PM10 concentrations were higher for the High-Volume sampler and TEOM monitor than the E-sampler. PM10 concentrations, however, were similar for the High-Volume sampler and TEOM monitor as well as for the BSNE samplers and DustTrak monitors. Similarity in PM10 concentrations measured by the High-Volume sampler and TEOM monitor was expected as the USEPA designates the TEOM monitor as being equivalent to the High-Volume sampler (USEPA, 2002). Relative differences among the five instruments in measuring PM10 concentration varied with height above the eroding surface. For example, the BSNE sampler appeared to overestimate PM10 concentration while the DustTrak monitor, E-sampler, and TEOM monitor appeared to underestimate PM10 concentration as compared with the High-Volume sampler over the range of concentrations measured at a height of 1 m during erosion events (Fig. 4). Indeed, PM10 concentration measured at a height of 1 m was 47% higher for the BSNE sampler and respectively 45, 54, and 23% lower for the DustTrak monitor, E-sampler, and TEOM monitor as compared with the High-Volume sampler. However, all instruments appeared to underestimate PM10 concentration, as compared with the High-Volume sampler, over the range of concentrations measured at a height of 2 and 3 m during erosion events (Fig. 4). PM10 concentration measured at a height of 2 and 3 m ranged from 6 to 57% lower for the BSNE sampler, 36–70% lower for the DustTrak monitor, 70–87% lower for the E-sampler, and 16–30% lower for the TEOM monitor as compared with the High-Volume sampler. As portrayed in Fig. 4, a decrease in the slope estimate of the relationship between PM10 concentration measured by the High-Volume sampler and the BSNE sampler and E-sampler occurred over the height at which we measured PM10 concentration (1–3 m). This systematic decrease in slope estimate of the relationship between PM10 concentration measured by the High-Volume sampler and the BSNE sampler and E-sampler with height suggested possible changes in the performance of the High-Volume sampler or the BSNE sampler and E-sampler with height. In contrast, the slope estimate of the relationship between PM10 concentration measured by the High-Volume sampler and the DustTrak and

3.2.1. Uniformity in wind speed and PM10 concentration The USEPA (2016) specifies standards for uniformity in wind speed and PM10 concentration when using wind tunnels for evaluating performance of PM10 instruments. We examined the uniformity in wind speed and PM10 concentration within the 0.3 × 0.9 m sampling zone by determining the variability in these characteristics at 24 positions within the sampling zone. According to USEPA criteria, wind speed and PM10 concentration at all positions in the sampling zone must be within 10% of the mean of all positions. In addition, the coefficient of variation in wind speed and PM10 concentration across all positions must be within 10%. We found that these criteria were generally met within the sampling zone of the wind tunnel (Table 4). Wind speed did not vary by more than 10% across all positions in the sampling zone and the coefficient of variation in wind speed was < 10%. Although the coefficient of variation in PM10 concentration was < 10%, PM10 concentration at several positions in the sampling zone exceeded 10% of the mean of all positions. Thus, the redesign of the wind tunnel generally met the USEPA criteria in evaluating performance of PM10 instruments. Wind speed, as measured by the fixed-position pitot tubes adjacent to the sampling zone inside the wind tunnel, was relatively consistent across all replications and varied by no more than 1 m s−1. Although the target wind speed was 7 and 12 m s−1 for this study, the respective measured wind speed ranged from 7.3 to 7.8 m s−1 and 12.4 to 12.8 m s−1 across both sets of instruments. PM10 concentration, as measured by the fixed-position DustTrak samplers adjacent to the sampling zone, varied from 1.3 to 2.7 mg m−3 at the low target concentration and from 21.3 to 30.4 mg m−3 at the high target concentration across both sets of instruments. The lowest values within these ranges of concentration occurred at a target wind speed of 12 m s−1 while the highest values within these ranges of concentrations occurred at a target wind speed of 7 m s−1. In fact, at respective target wind speeds of 7 and 12 m s−1, PM10 concentration at the low target concentration ranged from 1.7 to 2.7 and 1.3 to 1.8 mg m−3 and at the high target concentration ranged from 24.2 to 30.4 and 21.3 to 25.8 mg m−3 across both sets of instruments While wind speed appeared to influence PM10 concentration, variations in PM10 concentration at wind speeds of 7 or 12 m s−1 could be due to adjustments made to the electronic control on the fertilizer box that metered the rate of injection of dust into the airstream. In addition, variations in PM10 concentration across replications at a given wind speed could be due to heterogeneity in soil PM10 content. We attempted to minimize the influence of these factors on variations in PM10 concentration by using the same control setting for the specific target PM10 concentration and 48

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Fig. 4. Relationship between PM10 concentration measured by the High-Volume sampler and BSNE sampler, DustTrak monitor, E-sampler, or TEOM monitor. The relationship was defined based upon PM10 concentration measured at a height of 1, 2, and 3m above an eroding surface. Each symbol represents one erosion event during the three year field study.

Table 4 Uniformity in wind speed and PM10 concentration within the sampling zone of the working section of the wind tunnel used to compare the performance of five PM10 instruments. Variable

Wind speed (m s−1) PM10 concentration (mg m−3) 1

Magnitude1

7 12 2 50

Coefficient of variation (%)

4.4 5.0 7.8 8.5

Deviation from the mean (%) Maximum

Minimum

8.5 9.0 9.9 10.9

0.1 0.4 2.9 2.6

Target wind speed and PM10 concentration inside the wind tunnel.

mechanically mixing the soil prior to the experiment. Nevertheless, due to these factors possibly influencing PM10 concentration, we used an ANOCVA to analyze for differences in PM10 concentration as measured by the five instruments. The time series in PM10 concentrations as measured by the DustTrak monitor, E-sampler, and TEOM monitor inside the wind tunnel during a single 20 min run is shown in the Fig. 5. The data were collected while maintaining a target wind speed of 12 m s−1 and PM10 concentration of 50 mg m−3. Some variation was observed in PM10 concentration over the course of this run, with the least variation being recorded by the E-sampler and the greatest variation being recorded by the TEOM monitor. The standard error in PM10 concentration ranged from 0.1 mg m−3 for the E-sampler and 0.2 mg m−3 for the DustTrak monitor to 1.5 mg m−3 for the TEOM monitor. The average concentration over the 20 min run was 22.5 mg m−3 for the DustTrak monitor, 38.0 mg m−3 for the E-sampler, and 79.1 mg m−3 for the TEOM monitor. These concentration are at least an order of magnitude greater than those observed 1 m above eroding surfaces in our field study (Fig. 4). However, these PM10 concentrations are similar to those

Fig. 5. Time series in PM10 concentrations measured by the DustTrak monitor (blue line), E-sampler (orange line), and TEOM monitor (black line) inside the wind tunnel during a single 20 min run. The data were collected while maintaining a target wind speed of 12 m s−1 and PM10 concentration of 50 mg m−3.

that have been measured near an eroding surface during extreme events in the region (Sharratt et al. (2007). 3.2.2. Effect of wind speed on measured PM10 concentration PM10 concentration measured by the five instruments appeared to be influenced by wind speed (Table 5). PM10 concentration decreased with an increase in wind speed across both sets of instruments, except for the E-sampler when exposed to a target PM10 concentration of 2 mg m−3. Averaged across all instruments tested in the wind tunnel, PM10 concentration decreased by 11% at a target PM10 concentration of 2 mg m−3 and by 17% at a target PM10 concentration of 50 mg m−3 as wind speed increased from 7 to 12 m s−1. Similarly, in combining data for both sets of each type of instrument, the slope estimate of the relationship between PM10 concentration and wind speed ranged from 49

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Table 5 PM10 concentration measured by five instruments within the sampling zone of a portable wind tunnel. Comparisons were made among two different sets of instruments, with concentrations of each set being measured at two targeted wind speeds and PM10 concentrations inside the sampling zone. Instrument set

1

Target wind speed (m s−1)

7 12

2

7 12

Target PM10 concentration (mg m−3)

Measured PM10 concentration (mg m−3)

2 50 2 50 2 50 2 50

−0.99 mg s m−4 for the E-sampler to −2.98 mg s m−4 for the HighVolume sampler at a target concentration of 50 mg m−3. In contrast, the slope estimate ranged from −0.02 mg s m−4 for the E-sampler to −0.15 mg s m−4 for the BSNE sampler at a target concentration of 2 mg m−3. Thus, PM10 concentration measured by all instruments in this study was influenced by wind speed. Inefficiencies of the instruments in collecting or trapping particulates at higher wind speeds may contribute to this observed response. For example, vortices on the leeward side of the omni-directional inlets used on the E-sampler, HighVolume sampler, and TEOM monitor may affect collection of airborne particulates at higher wind speeds. However, despite modifying inlets of the DustTrak monitor to achieve isokinetic sampling, PM10 concentration was influenced by wind speed.

BSNE

DustTrak

E-sampler

High Volume

TEOM

4.0 70.9 3.4 62.7 4.4 70.7 3.6 59.2

1.8 28.7 1.5 24.0 2.1 29.5 1.6 23.1

2.5 47.2 2.7 45.2 3.1 44.2 3.0 36.3

5.3 52.8 5.2 34.1 4.0 39.3 3.0 28.3

6.8 80.4 6.6 68.5 9.1 85.9 8.7 70.6

Table 6 Differences in PM10 concentration attributed to sources of variation in a split-split plot analysis of covariance (ANOCVA). An ANOCVA was used to assess the performance of five instruments in measuring PM10 concentration at two wind speeds and two PM10 concentrations inside a wind tunnel. Source of variation

Instrument

Target wind speed (m s−1)

3.2.3. PM10 comparison among instruments Apparent differences in PM10 concentration measured by the five instruments appeared consistent between the two groups on instruments. For example, at either target PM10 concentration (2 or 50 mg m−3) to which instruments were exposed, measured PM10 concentration was lowest for the DustTrak monitor and highest for the TEOM monitor (Table 5). PM10 concentration measured by the TEOM monitor was 180–444% higher than measured by the DustTrak monitor. Differences in PM10 concentration between these two instruments were accentuated at the lower target concentration. PM10 concentration measured by the BSNE sampler appeared higher than that measured by the DustTrak monitor, E-sampler, and High-Volume sampler, but only at a target PM10 concentration of 50 mg m−3. At a target PM10 concentration of 2 mg m−3, PM10 concentration measured by the BSNE sampler appeared higher than that measured by the DustTrak monitor and E-sampler. An ANOCVA was used to statistically analyze differences among the five instruments in measuring PM10 concentration. Differences in PM10 concentration among the five instruments attributed to sources of variation in the ANOCVA are listed in Table 6. The results from the ANOCVA are nearly identical for the two groups of instruments. PM10 concentration was highest as measured by the TEOM monitor and lowest as measured by the DustTrak monitor. Averaged across target PM10 concentration and wind speed treatments, PM10 concentration was 283% higher for the TEOM than DustTrak monitor. This contrasts with observations by Chung et al. (2001) who reported PM10 concentrations were greater for the DustTrak than TEOM monitor during a two-month long study at Bakersfield, California. PM10 concentration as measured by the BSNE sampler, E-sampler, and High-Volume sampler tended to be similar, although PM10 concentration was consistently higher (45%) for the BSNE sampler than E-sampler. These results contrast with those obtained from field observations. While our wind tunnel study indicated significant differences among instruments in measuring PM10 concentration, we found fewer differences among instruments in measuring PM10 concentration in the field. However, we did find evidence in the field that PM10 concentrations measured by the High-Volume sampler and TEOM monitor were higher than

Target PM10 concentration (mg m−3)

Variable

BSNE DustTrak E-sampler High-Volume TEOM 7 12 2 50

PM10 concentration (mg m−3)1 Instrument group 1

Instrument group 2

14.3b2 5.8d 10.0c 14.9b 22.4a 15.6a 9.2b 3.4b 48.8a

15.9b 6.9 d 10.9c 10.6c 26.2a 13.6a 11.2b 3.5b 43.9a

1

Values represent logarithmic transformed data. Means followed by different letters within a column for a given source of variation are significantly different at P ≤ 0.05. 2

concentrations measured by the E-sampler. Similar observations were found in our wind tunnel study (Table 6). PM10 concentrations measured by filter-type instruments (High-Volume sampler and TEOM monitor) tended to be higher than light-scattering instruments (DustTrak monitor and E-sampler) in both the field and wind tunnel. While technologies differ between filter-type and light-scattering instruments, several factors empirically determine PM10 concentration measured by light-scattering instruments. For example, particle size, shape and refractive index influence the mass scattering efficiency or amount of light scattered per unit concentration measured by light-scattering instruments (Chow et al., 2002). Although instruments are calibrated using a standard media (DustTrak monitor using ISO 12103-1, A1 test dust and E-sampler using polystyrene latex spheres), this calibration is applied across a range of environments which can create uncertainty in measurements. Differences in PM10 concentration as measured by the High-Volume sampler and TEOM monitor (Table 6) was surprising since the TEOM monitor is considered equivalent to the High-Volume sampler (USEPA, 2002). Averaged across wind speed and PM10 concentration treatments, the TEOM monitor measured a higher PM10 concentration than the High-Volume sampler. Ono et al. (2000) also reported 25–35% higher PM10 concentrations as measured by a TEOM monitor versus a high-volume sampler in the vicinity of Owens Lake, California. They noted these differences were apparent at both low (40 µg m−3) and at high (1000 µg m−3) PM10 concentration and attributed these differences to a change in the sampling cut point of the high-volume sampler at high wind speeds. While high-volume samplers are designed to have a 10-µm cut point at wind speeds < 7 m s−1, a decrease in the sampling cut point at wind speeds > 7 m s−1 would reduce the collection efficiency of the High-Volume sampler. Thus, since nearly 50% of the 50

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sediment in suspension is in the 11–32 um size range in the Columbia Plateau (Sharratt, 2011), a lower collection efficiency of coarse particles would reduce PM10 concentrations measured by the High-Volume sampler. Guo et al. (2009) also reported higher PM10 concentrations in cattle feedlots as measured by a TEOM monitor compared with a HighVolume sampler. Vega et al. (2003) observed higher PM10 concentrations from a TEOM monitor compared with a High-Volume sampler at five sites near Mexico City. Wanjura et al. (2008) speculated that the design of the sample inlet and isokinetic split of the sample flow that characterize the TEOM monitor result in a higher PM10 concentration as compared with a gravimetric sampler. The ANOCVA also indicated that PM10 concentration measured at a target PM10 concentration of 2 mg m−3 was lower than as measured at a target concentration of 50 mg m−3. While this was expected, wind speed appeared to influence PM10 concentration. In fact, when averaged across target PM10 concentration and instrument treatments, PM10 concentration was statistically lower at the higher wind speed (Table 6). PM10 concentration measured at a target wind speed of 12 m s−1 was 20–40% lower than as measured at a target wind speed of 7 m s−1. Zogou and Stamatelos (2012) also reported lower PM10 concentrations, as measured by a DustTrak monitor, at higher wind speeds. This observation has implications for measuring PM10 concentration in field environments where wind speeds can exceed those used in designing PM10 instruments. While the USEPA mandates testing of instruments at < 7 m s−1 (USEPA, 2016), wind speeds can exceed 15 m s−1 in the field in the inland Pacific Northwest (Table 1). For this reason, we have historically deployed omni-directional inlets of PM10 instruments with shields to minimize the influence of wind on PM10 measurements in the field.

between light scattering and gravimetric samplers for PM10 mass concentration in poultry and pig houses. Atmos. Environ. 111, 20–27. Cheng, Y.S., Irshad, H., McFarland, A.R., Su, W.C., Zhou, Y., Barringer, D., 2004. An aerosol wind tunnel for evaluation of massive-flow air samplers and calibration of snow white sampler. Aerosol Sci. Technol. 38, 1099–1107. Chow, J.C., Watson, J.G., Lowenthal, D.H., Richards, L.W., 2002. Comparability between PM2.5 and particle light scattering measurements. Environ. Monit. Assess. 79, 29–45. Chung, A., Chang, D.P.Y., Kleeman, M.J., Perry, K.D., Cahill, T.A., Dutcher, D., McDougall, E.M., Stroud, K., 2001. Comparison of real-time instruments used to monitor airborne particulate matter. J. Air Waste Manage. Assoc. 51, 109–120. Feng, G., Sharratt, B., Wendling, L., 2011. Fine particle emission potential from loam soils in a semiarid region. Soil Sci. Soc. Am. J. 75, 2262–2270. Fryrear, D.W., 1986. A field dust sampler. J. Soil Water Conserv. 41, 117–120. Goossens, D., Buck, B.J., 2012. Can BSNE (Big Spring Number Eight) samplers be used to measure PM10, respirable dust, PM2.5 and PM1.0? Aeolian Res. 5, 43–49. Guo, L., Maghirang, R.G., Razote, E.B., Tallada, J.G., Harner, J.P., Hargrove, W.L., 2009. Field comparison of PM10 samplers. Appl. Eng. Agric. 25, 737–744. Heist, D.K., Tolocka, M.P., Vanderpool, R.W., Peters, T.M., Chen, F.-L., Wiener, R.W., 2001. Changes in operating procedures for achieving aerosol concentration uniformity for PM2.5 and PM10 sampler testing. Aerosol Sci. Technol. 34, 430–432. Houser, C.A., Nickling, W.G., 2001. The emission and vertical flux of particulate matter < 10 µm from a disturbed clay-crusted surface. Sedimentology 48, 255–267. Hudson, T. and A. Cary. 1999. Dust storms causes deadly pileup on I-84. Tri-City Herald September 26:A1. Kjelgaard, J., Sharratt, B., Sundram, I., Lamb, B., Claiborn, C., Saxton, K., Chandler, D., 2004. PM10 emission from agricultural soils on the Columbia Plateau: comparison of dynamic and time-integrated field-scale measurements and entrainment mechanisms. Agric. For. Meteorol. 125, 259–277. Lehocky, A.H., Williams, P.L., 1996. Comparison of respirable samplers to direct-reading real-time aerosol monitors for measuring coal dust. Am. Ind. Hyg. Assoc. J. 57, 1013–1018. Mendez, M.J., Funk, R., Buschiazzo, D.E., 2016. Efficiency of Big Spring Number Eight (BSNE) and Modified Wilson and Cook (MWAC) samplers to collect PM10, PM2. 5 and PM1. Aeolian Res. 21, 37–44. Ono, D.M., Hardebeck, E., Parker, J., Cox, B.G., 2000. Systematic biases in measured PM10 values with U.S. Environmental Protection Agency-approved samplers at Owens Lake, California. J. Air Waste Manage. Assoc. 50, 1144–1156. Pietersma, D., Stetler, L.D., Saxton, K.E., 1996. Design and aerodynamics of a portable wind tunnel for soil erosion and fugitive dust research. Trans. Am. Soc. Agric. Eng. 39, 2075–2083. Prueger, J.H., Kustas, W.P., 2005. Aerodynamic methods for estimating turbulent fluxes. Micrometeorology in Agricultural Systems. Agronomy Monograph No. 47. American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America, Inc., Madison, WI, USA. Ranade, M.B., Woods, M.C., Chen, F.-L., Purdue, L.J., Rehme, K.A., 1991. Wind tunnel evaluation of PM10 samplers. Aerosol Sci. Technol. 13, 54–71. Ravi, S., Zobeck, T.M., Over, T.M., Okin, G.S., D’Odorico, P., 2006. On the effect of moisture bonding forces in air-dry soils on threshold friction velocity of wind erosion. Sedimentology 53, 597–609. Saxton, K., Chandler, D., Stetler, L., Lamb, B., Claiborn, C., Lee, B.-H., 2000. Wind erosion and fugitive dust fluxes on agricultural lands in the Pacific Northwest. Trans. Am. Soc. Agric. Eng. 43, 623–630. Schillinger, W.F., Young, D.L., 2004. Cropping systems research in the world’s driest rainfed wheat region. Agron. J. 96, 1182–1187. Sharratt, B.S., 2007. Instrumentation to quantify soil and PM10 flux using a portable wind tunnel. In: Moody, L. (Ed.), Proceedings of the International Symposium on Air Quality and Waste Management for Agriculture. American Society of Agricultural and Biological Engineers Publication Number 701P0907cd. Sharratt, B., 2011. Size distribution of windblown sediment emitted from agricultural fields in the Columbia Plateau. Soil Sci. Soc. Am. J. 75, 872–878. Sharratt, B., Edgar, R., 2011. Implications of changing PM10 air quality standards on Pacific Northwest communities affected by windblown dust. Atmos. Environ. 45, 4626–4630. Sharratt, B., Lauer, D., 2006. Particulate matter concentration and air quality affected by windblown dust in the Columbia Plateau. J. Environ. Q. 35, 2011–2016. Sharratt, B., Feng, G., Wendling, L., 2007. Loss of soil and PM10 from agricultural fields associated with high winds on the Columbia Plateau. Earth Surf. Processes Landforms 32, 621–630. Sharratt, B.S., Feng, G., 2009. Windblown dust influenced by conventional and undercutter tillage within the Columbia Plateau, USA. Earth Surf. Processes Landforms 34, 1323–1332. Sharratt, B., Wendling, L., Feng, G., 2010. Windblown dust affected by tillage intensity during summer fallow. Aeolian Res. 2, 129–134. Sharratt, B.S., Vaddella, V.K., Feng, G., 2013. Threshold friction velocity influenced by wetness of soils within the Columbia Plateau. Aeolian Res. 9, 175–182. USEPA, 2002. Procedures for Determining Comparability Between Candidate Methods and Reference Methods. 40 CFR, Part 53, Subpart C, Federal Code of Regulations. U.S. Government Printing Office, Washington, D.C. USEPA, 2016. Ambient Air Monitoring Reference and Equivalence Methods. 40 CFR, Part 53, Section 53.62, Federal Code of Regulations. U.S. Government Printing Office, Washington, D.C. Vega, E., Reyes, E., Wellens, A., Sanchez, G., Chow, J.C., Watson, J.G., 2003. Comparison of continuous and filter based mass measurements in Mexico City. Atmos. Environ. 37, 2783–2793. Vincent, J.H., 2007. Aerosol Sampling, Science, Standards, Instrumentation and Applications. John Wiley & Sons, West Sussex, England, pp. 489–513.

4. Conclusion The performance of five instruments was tested in the field and laboratory in measuring PM10 concentrations above eroding soil surfaces. Significant differences were found among instruments in measuring PM10 concentrations in the field or wind tunnel. PM10 concentrations measured by the High-Volume sampler and TEOM monitor were higher than those measured by the E-sampler. Although field observations suggested similarity between the High-Volume sampler and TEOM monitor in measuring PM10 concentrations, the TEOM monitor measured higher PM10 concentrations than the High-Volume sampler in the wind tunnel. This observation is in agreement with past studies and suggests possible dissimilarities exist in instrument performance between federal reference and equivalence methods in measuring PM10. Instruments also appeared to be sensitive to wind speed when measuring PM10 concentration inside the wind tunnel. Variations among instruments in measuring PM10 concentration, even under controlled environmental conditions, suggests there are risks associated with using multiple instrument types to jointly measure PM10 concentration in the field or laboratory. In addition, caution should be exercised when measuring PM10 concentration by the same instrument type but at different wind speeds. While improvements in the operation of instruments continue to evolve, such improvements likely will have little influence on the results of this study unless changes are made to the design characteristics (e.g., sample inlet, optics) of the instruments. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.aeolia.2018.01.006. References Baas, A.C.W., Sherman, D.J., 2005. Formation and behavior of Aeolian streamers. J. Geophys. Res. 110, F03011. http://dx.doi.org/10.1029/2004JF000270. Cambra-Lopez, M., Winkel, A., Mosquera, J., Ogink, N., Aarmink, A., 2015. Comparison

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dust emissions. Aerosol Air Qual. Res. 11, 331–350. Wentz, J.W., Sinclair, R.E., 1981. Distribution of extreme winds in the Bonneville Power Administration Service area. J. Appl. Meteorol. 20, 1400–1411. Zogou, O., Stamatelos, A., 2012. Analysis of data from ambient PM10 concentration monitoring in Volos in the period 2005–2010. Am. J. Environ. Eng. 2, 97–108.

Wanjura, J.D., Shaw, B.W., Parnell Jr., C.B., Lacey, R.E., Capareda, S.C., 2008. Comparison of continuous monitor (TEOM) and gravimetric sampler particulate matter concentrations. Trans. Am. Soc. Agric. Biol. Eng. 51, 251–257. Watson, J.G., Chow, J.C., Chen, L., Wang, X., Merrifield, T.M., Fine, P.M., Barker, K., 2011. Measurement system evaluation for upwind/downwind sampling of fugitive

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