Published in J. Environ. Qual. 33:1653-1661 (2004).
© ASA, CSSA, SSSA
677 S. Segoe Rd., Madison, WI 53711 USA
TECHNICAL REPORTS
Ecological Risk Assessment
Enzyme Activities and Arylsulfatase Protein Content of Dust and the Soil Source
Biochemical Fingerprints?
V. Acosta-Martínez* and
T. M. Zobeck
USDA-ARS, Plant Stress and Water Conservation Laboratory, 3810 Fourth Street, Lubbock, TX 79416
* Corresponding author (vacostam{at}lbk.ars.usda.gov).
Received for publication October 17, 2003.
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ABSTRACT
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Little is known about the potential of enzyme activities, which are sensitive to soil properties and management, for the characterization of dust properties. Enzyme activities may be among the dust properties key to identifying the soil source of dust. We generated dust (27 and 7 µm) under controlled laboratory conditions from agricultural soils (0–5 cm) with history of continuous cotton (Gossypium hirsutum L.) or cotton rotated with peanut (Arachis hypogaea L.), sorghum [Sorghum bicolor (L.) Moench], rye (Secale cereale L.), or wheat (Triticum aestivum L.) under different water management (irrigated or dryland) and tillage (conservation or conventional) systems. The 27- and 7-µm dust samples showed activities of ß-glucosidase, alkaline phosphatase, and arylsulfatase, which are related to cellulose degradation and phosphorus and sulfur mineralization in soil, respectively. Dust samples generated from a loam and sandy clay loam showed higher enzyme activities compared with dust samples from a fine sandy loam. Enzyme activities of dust samples were significantly correlated to the activities of the soil source with r > 0.74 (P < 0.01). The arylsulfatase proteins contents of the soils (0.04–0.65 mg protein kg–1 soil) were lower than values reported for soils from other regions, but still dust contained arylsulfatase protein. The three enzyme activities studied, as a group, separated the dust samples due to the crop rotation or tillage practice history of the soil source. The results indicated that the enzyme activities of dust will aid in providing better characterization of dust properties and expanding our understanding of soil and air quality impacts related to wind erosion.
Abbreviations: LDGASS, Lubbock Dust Generation, Analyses and Sampling System • MANOVA, multivariate analyses of variance • PM10, particles with <10-µm aerodynamic diameter • PN, p-nitrophenol
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INTRODUCTION
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FUGITIVE (AEOLIAN) DUST emitted from wind-induced erosion of agricultural soils in semiarid regions of the United States not only presents problems of low visibility and irritating conditions, but also significantly threatens human health and the air and soil quality. Among various particle sizes of dust, the United States Federal Government issued regulations governing particles with 
10-µm aerodynamic diameter (PM10) as a primary air pollutant according to the National Ambient Air Quality Standards (NAAQS). The PM10 is capable of being transported long distances away from its source by the wind, which presents potential impacts over a large area during its travel and for a long time. The PM10 can potentially carry the most active labile organic fractions of soil, which can significantly impact the quality of the soil source. In addition, environmental health studies have indicated that PM10 is inhaled deeply enough into the human lower respiratory tract that it may adversely impact human health (Gordian et al., 1996; Pope et al., 1996).
The air and soil quality problems induced by wind erosion are unresolved, in part, because current technology does not enable identification of nonpoint sources of pollution where control measures are needed. Field studies of aeolian dust produced at or near the source of intense dust storms are difficult to conduct. Previous studies have reported dust properties of particles larger than PM10 or that contain a diverse particle size distribution (Gillette and Walker, 1977; Nickling, 1983; Fryrear, 1995; Gillies et al., 1996; Leys and McTainsh, 1996; Stout and Zobeck, 1996; Sonnleitner and Schinner, 2003). Few recent studies have presented limited data on PM10 generated during dust storms (Stetler et al., 1994; Stetler and Saxton, 1995). However, much more data is needed to characterize the properties of dust in the range of PM10 and to relate dust to the soil source. The USDA-ARS in Lubbock, TX, developed the Lubbock Dust Generation, Analyses and Sampling System (LDGASS) to simulate dust emissions generated by applying known kinetic energy to a dust source sample (Gill et al., 1999). Studies showed that dust collected by the LDGASS was correlated with dust generated by abrasion in a wind tunnel (Amante-Orozco, 2000). The approach of a dust generator has been used to estimate potential dust production in soils surrounding the Southern Aral Sea basin, Uzbekistan (Singer et al., 2003), and to relate the effect of clay and carbonate contents on the aerosol of PM10 and PM2.5 production from eight semiarid regions of the Southern High Plains of the United States (Amante-Orozco and Zobeck, 2002a, 2002b).
Little information exists on the biochemical properties of dust from various locations and management systems. Recent work using fatty acid methyl ester (FAME) profiles of dust samples suggested fatty acid fingerprint patterns could be useful for differentiating soils from various locations and different management systems (Zobeck et al., 1997; Kennedy and Busacca, 1998). A recent study reported differences in microbial respiration, microbial biomass C, and arylsulfatase activity between two soil deposits originated from wind erosion that differed in their organic C content and particle size distribution (Sonnleitner and Schinner, 2003).
The determination of the soil enzyme activities requires simple assay procedures and has reflected the management history of soils (Bandick and Dick, 1999; Klose et al., 1999; Ndiaye et al., 2000; Acosta-Martínez et al., 1999, 2003a, 2003b). Further, a new method for the estimation of arylsulfatase protein content in soil, based on the specific activity of a reference enzyme, could be applied for dust analyses (Klose and Tabatabai, 1999). Among the several enzymes that participate in soil organic matter decomposition and nutrient transformation, there are key enzymes to these soil processes that could be studied in dust. For example, ß-glucosidase, phosphatase, and arylsulfatase are widely distributed in nature, and play important roles in cellulose degradation and phosphorus and sulfur mineralization in soil, respectively. Thus, the objectives of our study were to (i) characterize the activities of ß-glucosidase, arylsulfatase, and alkaline phosphatase, and arylsulfatase protein content of dust samples generated under laboratory conditions from soils with different agricultural management history and (ii) assess the relationship between these biochemical properties of dust and the soil source to evaluate their potential as biochemical fingerprints of the soil source.
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MATERIALS AND METHODS
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Soil Sampling and Sites Description
The soil samples used were taken in January 1996, after the growing season, from commercial grower fields and research plots in West Texas (southwest of Lubbock, Texas, USA), USA. The surface soils in this semiarid region generally have a high sand content (45–95%) and pH (>7.0), and low organic matter content (<1%). The details of the sampling approach and management history of the soils are described in Acosta-Martínez et al. (2003b).
The soils used were an Amarillo fine sandy loam and sandy clay loam (fine-loamy, mixed, superactive, thermic Aridic Paleustalfs) and an Acuff loam (fine-loamy, mixed, superactive, thermic Aridic Paleustolls). According to a laser diffraction particle size analyzer (Beckman-Coulter [Fullerton, CA] LS-230), the fine sandy loam contains 10, 3, and 87% of clay, silt, and sand, respectively. The sandy clay loam contains 26, 20, and 54% of clay, silt, and sand, respectively. The loam contains 27, 32, and 41% of clay, silt, and sand, respectively.
The management history of the soils varied with crop rotation, water management, and tillage practices, for at least a period of two years before the time of sampling. Rotations were continuous cotton or cotton rotated with peanut, sorghum, rye, or wheat in different combinations. The fields were either dryland or irrigated. In the conventional tilled fields, the stalks of the first crop in the rotation were shredded and disked in December, moldboard (fine sandy loam and sandy clay loam) or deep chisel (loam) plowed in February, herbicide incorporated with a spring-tooth chisel followed by listing (creates 20- to 30-cm-high planting beds) in March, and rod-weeding before planting in early May. After planting in May, a rotary hoe was used for wind erosion control and to break the crust in May and June. Field cultivation was done twice, in June and July. The conservation tillage, which may also be specified as reduced or no-tillage, was applied as follows:
- (i) Continuous cotton, irrigated or dryland, reduced tillage: cotton stalks were shredded, preplant herbicide was added to the old rows and cultivated as needed for weed control.
- (ii) Terminated (refers to herbicide treatment to kill vegetation) rye or wheat–cotton, irrigated or dryland, no-tillage: rye or wheat planted into cotton stalks following harvest and chemically treated 2 to 4 wk before cotton planting, and cotton then planted into the rye or wheat residue.
- (iii) Sorghum–cotton, irrigated or dryland, conservation tillage system: cotton planted into previous sorghum crop residue.
- (iv) Wheat–cotton, irrigated or dryland, conservation tillage system: wheat grown for grain, the wheat stubble left standing and cotton planted into wheat stubble the following year.
Dust Generation
The dust samples were generated from the soils under controlled laboratory conditions using the LDGASS dust generator (Fig. 1). The LDGASS works by applying kinetic energy by gravity to a dust source sample to generate dust. Details of the dust generator design are described in Amante-Orozco and Zobeck (2002a)(2002b) and Singer et al. (2003). In brief, the dust generator consists of a dust-generating tube (1-m-long and 7.5-cm-square sheet metal tube) in which the soil is placed (Fig. 1A) and containing air flow valves at each end (Fig. 1B). The dust-generating tube is rotated 180 degrees from an initial vertical position approximately 27 times per minute (13.5 rpm). Kinetic energy is gained during the fall of the soil sample, and when the sample impacts the bottom of the dust-generating tube, a dust plume is generated. The dust is drawn out of the dust-generating tube by air flowing through a 1.5-cm-diameter orifice toward a U-tubing (Fig. 1C), and then enters to a settling chamber at its top (Fig. 1D). The settling chamber consists of a 45-cm-tall rectangular steel box with a 30-cm square and a pyramidal top (Fig. 1D). A vacuum located at the end of the system (suction side) originates the airflow (Fig. 1E), which enters the system through one of the two valves (2.54-cm-diameter openings) in the dust-generating tube (Fig. 1B). After passing through the settling chamber, airflow passes trough a cyclone separator, which collects most of the remaining dust particles leaving the settling chamber (Fig. 1E).
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Fig. 1. The Lubbock Dust Generation, Analyses and Sampling System (LDGASS) that contains (A) a dust-generating tube, (B) two valves, (C) U tubing, (D) a settling chamber, and (E) two dust filters and an air flow meter to control attached vacuum. The 27- and 7-µm dust samples were collected in (C) and (D), respectively.
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Two dust samples were collected from different locations in the LDGASS to obtain samples differing in size distribution. The airflow of the LDGASS was adjusted to 0.014 m3 min–1, and dust was generated three times during 20-min intervals. The suction rate was selected to ensure that only suspended dust was withdrawn from the dust-generating tube.
The first dust sample was collected from the U-tubing located between the dust-generating tube and the settling chamber (Fig. 1C). One dust sample was generated from (120 g of soil) each of the three soil field replicates sampled. Among the three soils studied, this dust sample was on average 10% < 1.4 µm, 25% < 5.4 µm, 75% < 39.9 µm, and 90% < 65.5 µm as determined by a laser diffraction particle size analyzer. On average, this dust sample had a mean particle size of 27 µm. The dust samples were sieved through a 106-µm sieve (USA Standard Testing Sieve, N. 150, <150 mesh) to homogenize this sample and remove any organic material or particles > 106 µm. The remaining soil sample in the dust-generating tube was also sieved to determine the total amount of <106-µm particles in 120 g of soil, including the amount of dust generated.
The second dust sample was taken from the bottom of the settling chamber (Fig. 1D). This dust sample was on average, among the three soils, 10% < 0.9 µm, 25% < 2.3 µm, 75% < 9.8 µm, and 90% < 15 µm. This dust sample was not sieved, and had a mean particle size of 7 µm, which falls in the range of PM10. This dust sample was generated by combining the three soil field replicates (360 g of soil) sampled to collect enough material for the enzyme assays.
Soil and Dust Analyses
The enzyme activities, pH, organic C, and total N of the soils are reported in Table 1 as determined by Acosta-Martínez et al. (2003b). In brief, organic C and total N were determined in air-dried soil (<180 µm) in a Elementar (Hanau, Germany) Vario Max CN-analyzer. In brief, each enzyme activity was assayed in 1 g of air-dried soil (<2 mm) using a final concentration of 10 mM of the specific enzyme substrate (p-nitrophenyl-derivate), buffered at the enzyme optimal pH, and incubated for 1 h at 37°C (Tabatabai, 1994). The product of the three enzyme reactions (PN, p-nitrophenol) was determined colorimetrically at 400 nm in a spectrophotometer. The samples were assayed in duplicates, and a control with soil was included in each assay, where the substrate was added after the incubation step. The soil controls were used to account for product released by chemical hydrolysis.
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Table 1. Classification, management, and selected properties of the semiarid agricultural soils used to generate the dust samples.
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The same procedure of soil enzyme assays was followed for the enzyme assays on dust. Because a small amount of dust was generally obtained, dust was mixed with inert glass beads (Potters Industries, Valley Forge, PA) of the same average size of the dust samples to facilitate the analyses. In general, 30-g (3 g of 27-µm dust and 27 g of 27-µm glass beads) and 6-g (1 g of 7-µm dust and 5 g of 7-µm glass beads) samples were generated for the assay of the three enzymes studied. Preliminary studies showed that dilutions greater than 13 times with inert glass beads would not be efficient enough for enzyme activity analyses, especially for arylsulfatase activity. Additional dust was mixed with the inert glass beads for representative samples used to include dust controls with similar purpose of soil controls. In summary, for each enzyme assay, the amount of dust analyzed was 0.500 g (fine sandy loam) or 0.833 g (sandy clay loam and loam) for 27-µm dust samples, and 0.075 g (fine sandy loam) or 0.166 g (sandy clay loam and loam) for 7-µm dust samples. The dust samples were analyzed in duplicates. Determination of the enzyme activities in the glass beads, without dust, showed the same activity levels when substrate was added before and after the incubation, demonstrating that the glass beads were inert. The enzyme activities were expressed in mg of PN released kg–1 dust or soil (moisture-free basis) h–1.
The arylsulfatase protein content in soil and dust was estimated according to the approach of Klose and Tabatabai (1999). In brief, arylsulfatase activity was determined as described by Tabatabai (1994). The arylsulfatase content in dust and soil was determined as the arylsulfatase activity in soil or dust divided by the arylsulfatase reference protein activity of Roman snail (Helix pomatia), which was 40.3 mg PN mg–1 arylsulfatase protein h–1 (Klose and Tabatabai, 1999). This method assumes the chemistry of arylsulfatase in soil or dust is similar to the reference proteins.
Statistical Analyses
Analyses of variance (ANOVA), multivariate analyses of variance (MANOVA), and mean separation by least significant differences (LSD) were performed using the general linear model (SAS Institute, 1999) to determine significant effects of crop rotation and tillage practices for the dust samples. In addition, linear regression analyses were performed to determine significant relationships between the enzyme activities of soil and dust samples.
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RESULTS
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Soil Source Properties
The properties of the soils used were reported by Acosta-Martínez et al. (2003b) and a summary is presented in Table 1. The MANOVA showed significant differences in pH, organic C, total N, and the activities of ß-glucosidase, ß-glucosaminidase, and arylsulfatase in the fine sandy loam compared with the loam and sandy clay loam (Acosta-Martínez et al., 2003b). Acosta-Martínez et al. (2003b) found higher organic C, and in most cases total N, in soils under crop rotations and conservative tillage practices than in soils under continuous cotton and conventional tillage, but these differences were not always significant. However, there were generally higher (P < 0.05) soil enzyme activities under crop rotations compared with continuous cotton and under conservation tillage practices compared with conventional tillage (Acosta-Martínez et al., 2003b). Their study found no particular trend on the chemical properties and enzyme activities in irrigated or dryland soils.
Dust Generated from the Soils
The fine sandy loam contained lower amounts of the <106-µm particles (including the dust generated) compared with the sandy clay loam and loam, and generated less dust than the other soils (Table 2). The amount of 27-µm dust generated with respect to the <106-µm fraction of the 120-g soil sample corresponded on average to 20, 28, and 20% for the fine sandy loam, sandy clay loam, and loam, respectively.
Even though the 7-µm dust samples were generated by combining three soil field replicates (a total of 360 g of soil), the amount generated from a 120-g sample was 0.1 to 0.2, 0.5 to 0.9, and 0.3 to 0.6 g in the fine sandy loam, sandy clay loam, and loam, respectively. On average, the amount of 7-µm dust generated from the <106-µm fraction of the 120-g soil samples corresponded to 0.9, 1.7, and 0.9% for the fine sandy loam, sandy clay loam, and loam, respectively.
Enzyme Activities of Dust
For both dust fractions (27 or 7 µm), ß-glucosidase, alkaline phosphatase, and arylsulfatase activities were higher in dust generated from the loam and sandy clay loam compared with dust generated from the fine sandy loam (Fig. 2). Linear regression analyses demonstrated that the enzyme activities of dust samples were significantly correlated to the activities of their soil source, with r values that ranged from 0.74 (P < 0.01) to 0.96 (P < 0.001) (Fig. 2). The equations of the linear regression analysis always revealed a smaller slope for the fine sandy loam compared with the other soils, and thus, the fine sandy loam was always separated from the other two soils in the plots.
Comparing the two dust fractions, higher enzyme activities were found in the 7-µm dust than in the 27-µm dust samples. Comparing dust and soil, enzyme activities were higher in the 27- and 7-µm dust samples than in their soil source, except for arylsulfatase activity of dust from the sandy clay loam and loam soil (Fig. 2).
Multivariate analyses of variance was not performed for the 7-µm dust samples because the three field soil replicates of each system had to be combined to generate enough material for the enzyme assays. The MANOVA for the 27-µm dust samples demonstrated significant differences in the set of enzyme activities investigated due to crop rotation and tillage practices of the soil source (Table 3). There was only significant crop rotation and tillage interaction for dust samples generated from the fine sandy loam. By itself, the activity of ß-glucosidase, alkaline phosphatase, or arylsulfatase did not separate the dust samples of the fine sandy loam due to crop rotations. In addition, ß-glucosidase activity did not separate dust samples from two of the three soils studied due to crop rotations.
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Table 3. Probability levels of the multivariate analyses of variance (MANOVA) of the enzyme activities of 27-µm dust samples.
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Arylsulfatase Protein Content of Dust
Arylsulfatase protein content was lower in dust from the fine sandy loam compared with dust from the sandy clay loam and loam (Table 4). The protein contents of the 27- and 7-µm dust samples generated from the fine sandy loam were up to three and five times higher than in the soil sample, respectively. The protein content in the 27-µm dust samples from the sandy clay loam and loam showed similar values to their soil sources. Similar to the relationship between arylsulfatase activity of dust and its soil sample, there was also a significant relationship between protein content of dust and its corresponding soil source (data not shown).
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DISCUSSION
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Soil Source Properties and Dust Generated from the Soils
A lower amount of dust was generated in the LDGASS from the fine sandy loam because of its higher sand content (87%), and thus, lower clay (10%) and organic C (1.36–2.80 g kg–1 soil) contents compared with the other two soils. These findings are in agreement with previous work with soils from the same region using the LDGASS (Zobeck et al., 1999).
Enzyme Activities of Dust
The similar enzyme activities of dust from the loam and sandy clay loam were due to the similarities in the organic C and clay contents of these two soils. These findings demonstrated a limitation in the use of enzyme activities and organic C for differentiation among dust samples. However, there was separation (lower slope) of the fine sandy loam (lower enzyme activities) from the sandy clay loam and loam in the linear regression analyses. These results, as explained previously, are due to the lowest organic C and clay contents of the fine sandy loam compared with the other soils. Thus, this separation of dust samples is in agreement with the fact that enzyme activities are mainly related to the clay fraction or finer soil fractions and organic C. In addition, most soil organic C is associated (40–60%) with the fine-silt size (2–20 µm) material and a lesser amount with the fine-sand size (50–250 µm) fraction (Ladd et al., 1990).
The separation of dust by the three enzyme activities studied (ß-glucosidase, alkaline phosphatase, and arylsulfatase) due to management practices was similar to the trends shown by another set of enzyme activities (ß-glucosidase, ß-glucosaminidase, and arylsulfatase) investigated in the soils (Acosta-Martínez et al., 2003b). The significant relationships between the enzyme activities of dust and soil may suggest that the enzyme activities of dust have potential to reflect the management history of the soil. Although this was demonstrated by the MANOVA for the three enzyme activities of 27-µm dust samples, the enzyme activities individually did not always show significant differences due to crop rotation and tillage practices of the soil source. Therefore, although ß-glucosidase activity is involved in cellulose degradation, ANOVA demonstrated that it was not possible to relate higher ß-glucosidase activity of the dust samples to a crop rotation history of the soil.
In this study, the ANOVA for the 27-µm dust samples showed that alkaline phosphatase activity was always sensitive, perhaps more sensitive than the other enzymes, to the differences of tillage practices of the soil source. Alkaline phosphatase activity was measured in the dust samples because it is induced in high pH soils, such as the typical semiarid soils used, for the hydrolysis of both organic phosphorus esters and anhydrides of phosphoric acid into inorganic phosphorus (Schmidt and Laskowski, 1961; Tabatabai, 1994; Acosta-Martínez et al., 2003a, 2003b). However, it could be valuable to detect both alkaline and acid phosphatase activities to distinguish if the source of dust was an alkaline or acidic soil.
Arylsulfatase Protein Content of Dust
The calculation of arylsulfatase protein content in soils or dust provides only an estimation value because there are several sources of enzymes in soils, and different isoforms of arylsulfatase in soils may express other activity rates than the reference protein used. Nevertheless, it has allowed comparisons of arylsulfatase protein content among soils (Klose and Tabatabai, 1999), and it may expand our understanding of dust characteristics. The protein content of the soils was much lower than values reported for 10 soils (average = 7 mg kg–1 soil) that varied in their clay and organic matter contents (Klose and Tabatabai, 1999). Arylsulfatase activity is generally the less predominant enzyme in soil (Tabatabai, 1994), especially in the low organic matter soils of semiarid regions (Acosta-Martínez et al., 2003b), but still arylsulfatase proteins were carried in the dust. The calculation of arylsulfatase protein content of dust supported the findings of other authors indicating that enzymes become stabilized and accumulated in soil (Tabatabai, 1994; Nannipieri et al., 2002; Sonnleitner and Schinner, 2003).
In summary, our study showed that dust in the range of PM10 (7 µm) and larger size (average of 27 µm) has potential to carry enzymes involved in cellulose degradation and phosphorus and sulfur mineralization in soil. These findings demonstrate the negative impacts of wind erosion on air and soil quality because particles greater than 20 µm can travel about 30 km from the source and particles finer than 10 µm could be transported thousands of kilometers during moderate (neutral atmosphere) wind storms (Pye, 1987). The three enzyme activities studied, as a group, separated the dust samples according to the different management history of the soil source, which is not possible by other characteristics of dust (i.e., particle size distribution). Results of our study indicated that the addition of enzyme activities and protein content to the battery of tests performed on dust material can provide better characterization of dust properties, and will expand our understanding of soil and air quality impacts related to wind erosion. However, knowledge of several enzyme activities of dust will be needed as biochemical fingerprints of dust because the location and source of enzymes in soil may depend on soil properties and the enzyme (Tabatabai, 1994; Nannipieri et al., 2002).
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ACKNOWLEDGMENTS
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The authors would like to thank Mr. Dean Holder and Mr. Terrance Grimard for the technical assistance provided with the LDGASS, and Mr. Kyle James for his assistance on the enzyme assays in dust samples.
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NOTES
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Trade names and company names are included for the benefit of the reader and do not infer any endorsement or preferential treatment of the product by USDA-ARS.
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, fine sandy loam; 
, sandy clay loam; 
, loam. Data on solid symbol (• = outlier) for ß-glucosidase activity (loam) were not included in calculating the regression line.