Characteristics associated with differences between undisturbed and industrially-disturbed soils

Soil Bid. Biochem. Vol. 25, No. 1I, pp. 14S1511, Printed in Great Britain. All rights reserved

0038-0717/93$6.00+ 0.00 Copyright 0 1993Pergamon Press Ltd

1993

CHARACTERISTICS ASSOCIATED WITH DIFFERENCES BETWEEN UNDISTURBED AND INDUSTRIALLY-DISTURBED SOILS M.J.

ROWELL* and L. Z. FLORENCE

Alberta Environmental Centre, Bag 4000, Vegreville, Alberta, Canada T9C lT4 (Accepted II May 1993) Summary-Disturbed soils from south and central Alberta that had been affected by oil well drilling, surface mining of coal and oil sands, hydrocarbon spills and pipeline construction were compared with undisturbed soils from similar areas. Biological and conventional soil assay methods were studied to determine their usefulness in reclamation management. Biological measurements were related to nutrient cycling (alkaline phosphatase, arylsulfatase, protease, arginine deaminase, mineralizable N and nitrification potential), microbial activity (dehydrogenase, invertase, biomass C and basal respiration rate) and organic matter content (extractable organic C and extractable color index). Conventional measurements included pH, electrical conductivity, organic C, CEC, mineral N, SO;-, P, saturation percentage and particle size distribution. Means of four characteristics were statistically different between disturbed and undisturbed soils. Electrical conductivity and SO:- were higher in disturbed soils while dehydrogenase and arylsulfatase activities were lower. The best multivariate subset of variables was associated with a discriminant function weighted most heavily by electrical conductivity, extractable organic C, alkaline phosphatase, P, CEC, extractable color index and arginine deaminase. Of the undisturbed soils 86% and 70% of the disturbed soils were correctly classified using canonical variate scores calculated from this subset. Most errors in classification occurred in the Dark Brown and Brown Chemozemic soil sub-groups; no errors in classification were observed for the Luvisolic soils. These results suggest that using this combination of biological and conventional measurements may provide a practical degree of assurance when determining the extent to which industrially-disturbed sites may have responded to remediation.

INTRODUCTION Information on the effect of soil disturbance on processes that affect soil-plant systems is important in long-term reclamation management. While many processes that are important in maintaining soil productivity have a substantial biological component most methods to assess disturbance and reclamation are chemical and physical in nature. Few biologicallyrelated methods have found widespread use in soil-

testing laboratories. Interpretation of the results is difficult due to the complex dynamics of microbial growth and activity in the soil. Biochemical measurements have been used to study anthropogenic effects on soils due to heavy metals (Tyler, 1976; Doelman and Haanstra, 1979), coal and oil shale mining (Hershman and Temple, 1979; Sorensen et al., 1981; Stroo and Jencks, 1982; Fresquez et al., 1987; Lindemann et al., 1989; Bentham et al., 1992), topsoil removal and storage (Speir and Ross, 1975; Ross et al., 1982), elemental sulfur application (Gupta et al., 1988), and acid precipitation (Press et al., 1985; Will et al., 1986; Jarvis et al., 1987). Because disturbance can affect the total soil environment, an attempt was made in this study to survey both biological and *Present address for correspondence: Norwest

Soil Research Ltd, 9938 67th Avenue, Edmonton, Alberta, Canada T6E OPS.

saa25/11-c

conventional soil variables. Biologically-related methods were selected to estimate the transformation of N (protease and arginine deaminase activity, mineralizable N and nitrification potential), P (alkaline phosphatase) and S (arylsulfatase). Basal respiration rate, biomass C, invertase and dehydrogenase were chosen as general indicators of microbial activity. Extractable organic C and color were included as measurements of the quantity and quality of humified material. Conventional techniques included pH, electrical conductivity, organic C, CEC, mineral N, SO:-, P, the water holding capacity at saturation and particle size distribution. Because of this multivariate data structure, discriminant analysis was used to statistically characterize the covariance among variables and between soil groupings. Multivariate methods have shown considerable promise in soil mapping and capability assessment (Lowe et al., 1987; Nolin et al., 1989) and in the study of biological relationships between soils (Stott and Hagedorn, 1980; Ross et al., 1975, 1982; Sarathchandra et al., 1984; Vekemans et al., 1989). A recent study of restored open-cast coalmine sites suggested that habitats could be classified by means of cluster analysis using the results of principal component analysis (Bentham et al., 1992). Three microbiological indices (soil dehydrogenase activity, ATP and ergosterol) discriminated between restored grassland and woodland sites, and comparable undisturbed habitats. This

1499

M. J. ROWELL and L. Z. FLORENCE

1500

Table 1. Soil types within each group and approximate equivalents in the Canadian, U.S.A. and FAO systems of classification (Agriculture Canada Expert Committee on Soil Survev. 19871 Group

Canadian

I

Black Chemozem

2 3 4

Dark Brown Chemozem Brown Chemozem Gray Brown Luvisol Gray Luvisol

5

Eutric Brunisol Dystric Bnmisol

was not possible using only physical and chemical characteristics such as pH, organic C, total N, moisture content, bulk density and textural analysis. Our objectives were: (i) to determine if methods designed to assess biological processes could be useful and complementary to information from conventional chemical and physical measurements; and (ii) to characterize the multivariate differences between industrially-disturbed and undisturbed soils. MATERIALS AND METHODS

Sample collection and preparation

Disturbed and undisturbed surface soils were collected between September and November of 1989 from south and central Alberta (68 in total). They included examples from the Brunisolic and Luvisolic orders and the brown, dark brown and black great groups of the Chernozemic order. Undisturbed Luvisols and Chernozems are predominantly used for agriculture while the Brunisols support a forest vegetation. Soil disturbance resulted from oil well drilling, surface mining of coal and oil sands, hydrocarbon spills and the construction of pipelines. Soil samples were air dried and ground to <2 mm. A more complete description of the samples is given in Tables 1 and 2. Chemical and physical methods

The methods used were from McKeague (1978) as recommended for Canadian soils unless otherwise specified. Soil pH was measured on a 1: 2.5 soil :water suspension using a glass electrode; electrical conductivity was measured on an extract from a saturated paste; organic C was determined using a LECO’” high temperature induction furnace; available P was measured using a 10 min extraction with 30 mM ammonium fluoride-60 rnM sulfuric acid solution at a 1: 5 soil extract ratio with phosphate measured by Technico+’ autoanalyzer using the method of Flannery and Markus (1969). CEC was measured using 1 M ammonium acetate at pH 7; mineral N was extracted for 30 min at a 1: 5 soil extract ratio using 2 M KC1 with NH:, NO; and NO; measured by TechniconM autoanalyzer using the method of Markus et al. (1982). Soluble SOi- was measured turbidometrically

U.S.A.

FAO

Udic Boroll Rendoll Typic Boroll Aridic Boroll Hapludalf Glossudalf Boralf

Chemozem Rendzina Kastanozem (typic) Kastanozem (aridic) Albic Luvisol

Cryochrept Eutrochrept Dystrochrept Cryochrept

Albic Luvisol Podzoluvisol Eutric Cambisol Dystric Cambisol

using a 1: 2 soil extract ratio by shaking with 10 mM CaCl, for 30 min; and particle size distribution was measured using a hydrometer method. Enzyme assays and other biological method

If possible the methods were scaled down and simplified to make them compatible with the high volume and automation that exists in modern soil testing laboratories. The method of sample preparation is well known to affect the measurement of enzyme activities (Bums, 1978). Field-moist samples are generally recommended rather than air-dried soils for interpretation of biological properties. Except where soils were pre-incubated, air-dried samples were used to conform with the normal preparation for chemical and physical methods. Lengthwise shaking at 100 oscillations min-’ was employed in the majority of enzyme assays since it gave more reproducible results than static incubation. Bacteriostatic agents were not used in most short-term assays. Three replicates of each assay and appropriate controls were run for each soil. Alkaline phosphatase. The method of Tabatabai and Bremner (1969) was used with slight modification. Reactions were carried out in screw-capped culture tubes containing 0.5 g of air-dry soil and 5 ml of Modified Universal Buffer at pH 11 containing 5 mM disodium p-nitrophenyl phosphate tetrahydrate and shaking at 37°C for 30min. The reaction was terminated by addition of 1 ml 500 mM CaCl, and 4ml 500m~ NaOH. After mixing and centrifuging the p-nitrophenol released was determined by measuring the absorbance of the extract at 420 nm. Controls were run by carrying out the same assay but with addition of substrate after addition of CaCl, and NaOH. Arylsulfatase. Conditions were similar to those outlined by Tabatabai and Bremner (1970) but with shaking and without toluene. Assays contained 1 g of air-dry soil, 5 ml of 500 mM acetate buffer at pH 5.8 containing 5 mM potassium p-nitrophenyl sulfate and were run at 37°C for 60min. The measurement of p-nitrophenol and preparation of controls were the same as described for alkaline phosphatase. Arginine deaminase. A modification of the aerobic method of Alef and Kleiner (1986) was used.

Characteristics associated with disturbed soils

Reactions were carried out without prior incubation in screw-capped culture tubes containing 1.6 g of airdry soil and 4 ml of 9 mu arginine at 40°C for 60 min. The soil was shaken for 30 min with 4 ml of 4 M KC1 and NH: in the centrifuged extracts measured as previously described. Controls were run under the same conditions to measure NH: initially present in the soil and the reagents. Tests indicated that neither nitrite or nitrate accumulated during the assay. Dehydrogenase. A modification of the method of Vekemans ei al. (1989) was used. Soil (1 g) was mixed

1501

with 2 ml of 0.5% 2,3,5-triphenyltetrazolium chloride (TX) in 50Om~ THAM buffer [Tris(hydroxymethyl)amino methane] at pH 7.6 and incubated statically in a screw-capped culture tube at 30°C. After 18 h the reduction product 2,3,Wriphenyl formazan (TPF) was recovered by extracting with 8 ml of methanol. The formazan concentration was estimated by measuring the color of the centrifuged extract at 480 nm relative to standard TPF solutions in THAM buffer. Controls were included where TX was added just prior to extraction with methanol.

Table 2. Distribution of samoles bv soil aroun and nature of disturbance Number of samples in each group

Sample locations and nature of reclamation practices

Soil groups* 4 5

1

2

3

Dt

UD

Paintearth coal mine: reclamation since 1983 has involved topsoiling, fertilization and seeding to grain, forage and pasture crops. Undisturbed soils were from areas adjacent to the mine.

0

8

0

0

Diplomat coal mine: reclamation since 1950s has involved a variety of methods including ridging, Iwelling, topsoiling, fertilization and seeding to grain, forage or rangeland crops. Undisturbed soils were from areas adjacent to the mining area.

0

6

2

0

0

0

0

8

3

Samples from surface mining area.9

Samples from

heavy oil extraction

II

oreas

Disturbed samples represented operational and experimental soil mixes used by Suncor Inc. since 1971 for erosion control on sandcovered tailings pond dikes. Reclamation included amendment with peat, mineral overburden, bitumen sludge and fertilizers and revegetation with trees, grasses and legumes. Undisturbed soils were taken from forested areas close to the mine.

0

0

0

0

11

9

2

2

2

2

0

0

3

3

2

4

4

0

0

5

5

An oil spill rehabilitation experiment started in 1973 at the University of Alberta Experimental Farm. Reclamation involved tillage, fertilization and revegetation with grain crops.

4

0

0

0

0

3

1

An experimental site set up in 1987 to study the disposal of diesel invert cuttings from oil well drilling by landfanning. Reclamation involved tillage, addition of mineral fertilizers and manure and reseeding with grasses and legumes.

0

0

0

4

0

3

1

Samples

token following

pipeline

construction

Reclamation involved topsoil salvage and replacement and revegetation to the previous land use. Undisturbed samples were taken from areas adjacent to the pipeline. Samples from

abandoned

oil well akilling

sites

Reclamation involved salvage of topsoil, replacement of topsoil, fertilization and revegetation to the previous land use once the drilling operations were concluded. Samples

im~olvbtg hydrocarbon

Representative

soils not affected

contamination

by industrial

Arable soils producing grains and forages. Soils from native range and pasture. Forested soils. *See Table 1 for details. tD = disturbed; UD = undisturbed.

dtkturbonce 3

2

2

0

0

11

0 0

0

2

:

0

0

2

0

0

0

I

0

1

1502

M. J. ROWELLand L.

Blank readings due to reagent impurities and extracted soil color were very low in all soils tested. Protease. Soil (1 g) was mixed with 2.5 ml of 100 mM THAM buffer at pH 8.1 containing 1Omg ml-’ of sodium caseinate and shaken for 60min at 50°C (Ladd and Butler, 1972). The reaction was terminated by addition of 1 ml of 17.5% trichloracetic acid. Tubes were centrifuged and 2ml of the supernatant mixed with 3.0 ml of 1.4 M Na,CO, and 1 ml of l/3 strength Folin reagent. The absorbance of the blue color was measured at 700 nm using tyrosine as a standard. Invertase. A modification of the method of Ross (1966) was used. Air-dry soil (1 g) was weighed into culture tubes and 0.5 ml of toluene added. After 15 min 2 ml of a 5% sucrose and 2 ml of 100 mM sodium acetate-potassium phosphate buffer at pH 5.5 was added. After 2 h at 40°C without shaking the tubes were cooled in ice water and centrifuged at 4°C. Glucose production was estimated enzymatically by reaction with glucose oxidase and peroxidase. A 100 /*l sample of the extract was added to 500 ~1 of enzyme-color reagent buffered at pH 4.2 with 100 mM sodium acetate and containing 1.33 mg ml-’ o-tolidine dihydrochloride, 2.9 units ml-’ peroxidase (Sigma Chemical Co., Horseradish type II, 220 units mg-‘) and 2.25 units ml-’ glucose oxidase (Sigma Chemical Co., Aspergillus niger, type V, 1500 units ml-‘). The reaction was allowed to continue to completion (25-35 min at 25°C) diluted with 2 ml of distilled water and the absorbance measured at 635 nm. Glucose (10-200 pg ml-‘) was used to cahbrate the method. Mineralizable nitrogen. A modification of the anaerobic incubation method of Waring and Bremner (1964) was used. Air-dry soil (1.6 g) was weighed into culture tubes and 4 ml of distilled water added. The tubes were capped and kept for 7 days at 40°C. 4 ml of 4M KC1 were added and the tubes shaken for 60 min. Ammonium and nitrate in the extracts were measured as described previously. Triplicate sets of untreated soil were extracted similarly to determine the residual mineral N prior to incubation. Extractable color index. The color intensity of organic extracts was used as an index of humification using an extraction method similar to that described by Anderson et al. (1974) but without separating fulvic and humic acid fractions. A 0.5 g sample was extracted with lOm1 of 100 mM sodium hydroxide-100 mM sodium pyrophosphate (pH 13) in screwcapped tubes for 16 h. The color intensity of the extract was measured at 400nm after diluting with 100 or 500 mM sodium bicarbonate to maintain the pH in the 8.1-8.5 range. The extractable color index was defined as the equivalent absorbance from 1 g of soil under these conditions. Extractable organic carbon. A 1 or 2 ml sample of centrifuged extract from the previous analysis was evaporated to dryness at 80°C and the organic C content determined using a modification of the

Z. FLORENCE

digestion method of Sims and Haby (1971) and the color measurement described by DeBolt (1974). 1 ml of 1 N potassium dichromate and 2 ml of cont. H2S04 was added to the residue and maintained at 80°C for 60 min. The cooled contents were diluted with 7 ml of distilled water and absorbance measured at 620 nm using sucrose as a standard. NitriJication potential. The production of NO; and NO; following the addition of (NH4)2S04 was determined using an aerobic incubation technique similar to that of Malhi and McGill (1982). Soils were incubated in triplicate at 50% of maximum water holding capacity (WHC) at 23°C. After 7 days ammonium sulfate was added at a concentration of 100 mg kg-’ of soil and incubation continued at 23°C for a further 7 days. Soils were shaken with 8 ml of 2 M KC1 (1: 5 soil extract ratio) for 30 min and NH:, NO; and NO; in the centrifuged extracts determined as previously described. Results were corrected for mineral N changes in similarly treated non-amended soils. Basal respiration rate. Soil (100 g air-dry) was kept at 50% of maximum WHC in aerated 1 litre jars at 23°C. Frequent measurements were made until a constant rate was recorded, normally between 7 and 14 days. The containers were closed and the CO, produced during 16 h was absorbed in vials containing 500 mM NaOH placed at the soil surface. CO? was determined by precipitating carbonate with excess BaCl, and titrating the residual base with HCl to the phenolphthalein end point. Biomass carbon. Soil incubated for 21 days (from basal respiration measurement) was treated using the fumigation-incubation technique with a 10 day incubation for both fumigated and control soil and a k, of 0.45 (Jenkinson and Ladd, 1981). Statistical

methods

For statistical analysis the soils were grouped into soil orders or great soil groups based on field observations, soil survey information or, in the case of some of the disturbed soils, on the dominant soil association prior to disturbance. The grouping variables were for soils that had been industrially-disturbed (D) or normal undisturbed soils (UD), and five soil groups: Black Chernozems (l), Dark Brown Chernozems (2), Brown Chernozems (3), Luvisols (4) and Brunisols (5). Methods were grouped into conventional and biological methods. The former were pH, electrical conductivity, organic C, CEC, NO,, NH:, SO:-, P, saturation percentage, sand and clay contents. Biological methods were alkaline phosphatase, arylsulfatase, protease, arginine deaminase, mineralizable N, nitrification potential, dehydrogenase, invertase, biomass C, basal respiration rate, extractable organic C and extractable color index. Data transformation. With the exception of pH (log hydrogen ion concentration), data for the 12 biologically-based assays and the 11 conventional measurements were transformed to common, base 10

Characteristics associated with disturbed soils

logarithms prior to analysis of variance and calculating canonical variate scores from discriminant analysis. Log transformation served to standardize measurement units among variables and reduced variance heterogeneity between soils classification variables (see Table 3). Analysis of variance. To meet objective (i) of the study, each of the biological and conventional assay methods was entered into analysis of variance (ANOVA). Where variables were significant at the 0.05 level or less, means were compared (at the 0.05 level) between the D and UD classes and among the five soil groups. Because the Type I error can become inflated as the number of ANOVAs increases with increasing number of soil variables to be analyzed, the error rate was controlled by the Bonferroni method (Jobson, 1991) to keep the Type I error at 0.05, or less. Multiple mean comparisons at the 0.05 level were done by the Tukey-Kramer test (Statistical Ar.alysis Systems, 1991). Multivariate discriminant analysis. Objective (ii) was sought by backward stepping discriminant analysis (Statistical Analysis Systems, 1988). Three different suites of variables were submitted: (1) biological only; (2) conventional only; and (3) biological and conventional combined. Variables meeting the criteria to remain in each particular discriminant function were then used to calculate coefficients for determining canonical variate scores for each soil sample. The unbiased, jackknife method for classifying each sample into either disturbed (D) or undisturbed (UD) was based on Lachenbruch and Mickey (1968); see Johnson and Wichern (1992) for detailed discussion and applications. Prior probabilities were set at D = UD = 0.5; no basis presently exists for setting these other than equal.

RESULTS AND DISCUSSION

Variation among soil groups Within the Canadian system of soil classification (Agriculture Canada Expert Committee on Soil Survey, 1987) Chemozemic orders are distinguished on the basis of surface color and depth of the organic-enriched surface Ah horizon. Both depth and organic matter concentration increase in the order Brown, Dark Brown to Black. Luvisolic soils have light-colored eluvial surface horizons and illuvial Bt horizons in which silicate clays have accumulated. They are distinguished from Chemozems by the depth and color of the Ah horizon and the depth and distinctiveness of the eluviated Ae horizon. Brunsolic soils show poor development and are intergrades between Regosolic soils and several other orders. They do not contain the clay accumulation of the Luvisolic Bt horizon and lack the physical and chemical characteristics of the Chernozemic Ah horizon. Previous studies have emphasized a gradation of organic characteristics between Chemozemic orders and a

1503

distinctness between Chemozems and Luvisols in terms of reflected surface color (Shields et al., 1968), extracted organic material (Anderson et al., 1974) and elemental distribution (Roberts et al., 1989). A summary of the data is given in Table 3. Five of the biological variables differed among the five soil groups [Fig. l(a)-(e)]: nitrification potential, basal respiration rate, extractable organic C, extractable color index and invertase. Eight variables from conventional methods exhibited significant differences among the five soil groups [Fig. l(f)-(m)]: organic C, CEC, pH, NO;, NH:, P, sand and clay contents. The results observed from conventional assay methods are not totally unexpected because the soil classification criteria for the five groups are related to organic matter, texture and nutrient supply. Within the Chernozemic order NH:, pH, basal respiration rate, organic C, extractable color index and extractable organic C values followed the sequence Black, Dark Brown to Brown. This is probably related to the influences of vegetation and climate on organic matter characteristics and to variability in moisture and salt movement within surface horizons. Higher nitrification potential and invertase activity and, to a lesser extent, available NH:, NO; and P tended to be associated with Chemozemic soils that are considered to have greater productive potential than Luvisolic or Brunisolic soils. Nitrification potential and basal respiration rate, indicated overall trends in mean response: (1) mean nitrification potential tended to decrease from agricultural Black and Dark Brown Chemozems to Brown Chernozems, Luvisols and forested Brunisols, whereas (2) basal respiration rate exhibited the opposite trend. Differences separating Black Chernozemic soils or Brunisolit soils from all others were sand, clay, organic C and CEC. Trends from non-forested soils to the forested Brunisols were indicated most by lower nitrification potential, CEC, P and NO, and, less distinctly, by reduced invertase activity. These results contrasted with a lack of significance with certain biological tests such as mineralization potential, biomass C and dehydrogenase and apparently contradictory results with basal respiration rates. High basal respiration rates were measured for Brown and Brunisolic samples under the laboratory test conditions where temperature and moisture were near optimum. Natural respiration rates in the field, where conditions are below optimum, might result in lower respiration in relation to Black and Dark Brown Chernozems despite the natural accumulation of organic C as litter or poorly humified material. Many of the disturbed Brunisolic samples from oilsand extraction sites contained potentially-decomposable material in the form of peat added to assist revegetation. The fact that cultivation is known to lead to higher losses of organic matter and soil deterioration (McGill et al., 1988) could result in reduced respiration from Chemozems in comparison to other soils like Brunisols or Luvisols that

M. J. ROWLL and

1504

are uncultivated or have been cultivated for less time. In this study all the Chernozems and some Luvisols were cultivated while the Brunisols were all uncultivated. 1.45

Nitrificafion

.

L. Z. FLORENCE While being cognizant that the above ANOVAs do not account for the underlying sampling variation within soil groups due to the “disturbance factor”, it seemed prudent to avoid further subclassification

I

2.2-I Basal

.

1.8 1.6 Id

I 1’2

1.65 15.5

i

I

‘3’4

Extractable Organic C

I

I

5

1.2

(0) s .

3 2.8 2.6

1.45

2d 2.2

1.35

2

1.25

.

1.8 1.15

:

1.6 1.4

1.05 2

t

3

1

5

4

2

3

2

34

5

4

(CO

1.7 1.6 1.5 ld 1.3

J -1.5

1.2

lnvertase

.

,

1

I

i

2

t I

r 3

4

5

1.1

(e)

1

1

5

.

5.5

I 1 Soil Group

I 2

I

i-1 3

Soil Group Fig. I (a-h)

(0

4

5

0

Characteristics associated with disturbed soils

.

1.55

2.1

Nitrate-N

1.5

1.9

1.45

1.7

id 1.35

1.5

Ammonium-N

1..

1

*I

1.3 1.3

125 12

1.1

1.15 1.1 ’

0.9

.

I 1

2

.

.

2.05 ‘,

Phosphate-P

1.95’

I

.

I

3

1 4

I 5

’

(9

Sand %

$0

,

,

1.85 1.75 1.45

. 1.45

I

1

. I

2

II

3

4

5

0

Sail Group

1.9 1.8 1.7

-GB-

1.6 1.5 1.4 1.3

0

1.2 1.1

1

2

34

5

Soil Group

Fig. 1 (i-m) Fig. 1. AVOVA summaries and comparisons of means (vertical axis, log,,) among five soil groups for biological methods (a)-(e) and conventional methods (fHm). The horizontal rule is the overall mean response; soil group means are the rules across and inside each diamond, height of the diamond is the 95% confidence interval of the mean and the diimond width represents the proportional difference in sample sizes. Circles which are non-intersecting, or having an angle of intersection <90”, indicate class mean differences of P < 0.05.

terms in the ANOVA model. To include them could have led to spurious conclusions based upon small unequal sample sizes with means subject to unacceptable error. Our results, therefore, intend to refiect overall general relationships holding “disturbance” at a fixed average encountered in Alberta soils among these groups, bearing in mind that the samples were selected to include a greater pro~rtion of disturbed

sites than would be found selection of locations.

by a purely random

Individual mean d#erences undisturbed soils

between disturbed and

Two conventional (electrical conductivity SO$-) and two biological (dehydrogena~

and and

M.J. ROWELL and L.Z. FLORENCE

1506

arylsulfatase) methods were statistically different between D and UD soils (Fig. 2). Concentrations of dehydrogenase and arylsulfatase were greater in undisturbed than in disturbed soils. Were future sampling to reveal the same outcome, then, objective (ii), to characterize the multivariate differences between industrially-disturbed and undisturbed soils, could be greatly simplified by performing and independently testing only these four assays. The relationships with electrical conductivity, SO:-, dehydrogenase and arylsulfatase seem plausible when it is considered that soil disturbances of the nature studied often involve loss of topsoil and mixing of surface and subsurface horizons. This may result in a reduction in organic matter, an increase in salts dominated by sulfate and carbonate and, sometimes, an increase in clay and pH. Any changes in organic matter, clay and pH related to disturbance appear to be obscured by the larger differences of these characteristics between soil groups or by the use of organic amendments in soil reclamation mixes. The relationship between disturbance, SOi- and arylsulfatase may be complex. Poor correlation has been shown between arylsulfatase activity and sulfur mineralization (Tabatabai and Bremner, 1972; Kowalenko and Lowe, 1975). Moreover, reports show arylsulfatase activity being both unaffected

-I 1.2

i

1.08 -

_

1.07 -

.

1.06 1.05

1

Arylsulfatase

I .

. I

0

I

I

(a)

1

, 15-

I

i

I

1.05

1.09 -

- Dehydrqgenahe

0

(Tabatabai and Bremner, 1970; Frankenberger and Bingham, 1982; Wainwright, 1981; Han and Yoshida, 1982; Ganeshamurthy and Nielsen, 1990), reduced (Cooper, 1972; Chandramohan et al., 1974; Gupta et al., 1988) or even stimulated (Skiba and Wainwright, 1983) by increased SOi- concentrations. Individual results within this study showed a similar variability. Where SO:- is high, the observed activity may represent residual exoenzyme stabilized on inorganic surfaces or by reaction with organic soil constituents. Direct product inhibition by sulfate seems unlikely since it is thought that the inhibitory species is probably HOSO; rather than SOi- (Dodgson et al., 1982) and the pH of the arylsulfatase assay is not sufficiently acidic enough to expect high concentrations of the HOSO; species to be formed. It is more likely that the reduction of arylsulfatase activity is due to lower enzyme synthesis in response to higher availability of S for microbial growth or poorer conditions for microbial or plant growth following disturbance. Arylsulfatase seems to be sensitive to seasonal changes related to soil moisture and aeration. Activity has been shown to increase as dry soils are moistened (Cooper, 1972; Ross et al., 1984) and to decrease at saturation (Pulford and Tabatabai, 1988). The influence of soil moisture and water movement on arylsulfatase activity may be related to the extent

Electrical Conductivity

(b) Sulfate-S

i I

: : :

1.1

. .

I lundstubd

diirbed

TYPO

cc>

1Lndstubed’

dlawbed

TYPO

Fig. 2. AVOVA summaries and comparisons of means (vertical axis, log,,,) between industrially-disturb (D) and undisturbed (UD) soils. The horizontal rule is the overall mean response; soil order means are the lines across and inside each diamond, height of the diamond is the 95% confidence interval of the mean and the diamond width represents the proportional difference in sample sizes. Circles which are non-intersecting, or having an angle of intersection <90”, indicate class mean differences of P -z0.05.

1507

Characteristics associated with disturbed soils Table 3. Statistical summaries of data for biological and conventional methods

Coefficient of variance Variable name

Units of measurement

N’

mg NH:-N kg-’ day-’ mg N0j-N kg-’ day-’ mg NH:-N kg-’ day-’ rn~ tyrosine equiv kg-’ h-’ mg CO& kg-’ h-’ g C kg-’ mg TPF kg-’ h-’ $(Mp-nitrophenol gg’ h-’ PM p-nitrophenol g-l h-’ g C kg-’ absorbance gg’ ELMglucose g-’ h-’

68 54 66 49 54 50 68 53 68 68 68 68

OrganicC

%

CEC PH Electrical conductivity Saturation percentage Nitrate Ammonium Sulfate Phosphate Sand Clay

m-equiv 100 g-l -log [HI dS m-’ moisture % mg N kg-’ mg N kg-’ mg S kg-’ GOgP kg-’

68 68 68 68 68 68 68 68 68 68 68

SD

Min.

Max.

Raw?

Transformation

48.1 4.1 4.6 0.3 0.5 0.4 1.9 1.7 0.6 9.1 203.3 1.6

28.58 3.73 2.26 0.19 0.37 0.22 1.33 1.05 0.41 6.71 160.46 1.08

1.70 0.00 0.75 0.00 0.10 0.11 0.01 0.27 0.05 2.30 18.00 0.04

111.20 15.82 13.07 0.77 1.65 1.04 5.32 4.40 2.21 30.10 671 .OO 5.26

59.4 79.8 49.5 61.6 71.4 53.1 69.5 62.1 71.0 69.5 78.9 67.9

14.3 9.2 5.5 0.8 12.4 0.9 4.4 3.6 9.2 10.3 14.5 3.7

6.2 31.2 6.8 0.9 42.3 1.5 1.3 42.8 37.6 41.9 22.5

6.09 15.36 0.73 0.88 14.03 13.26 3.13 103.56 23.08 18.73 12.51

0.70 1.00 5.26 0.19 21.30 0.00 3.00 0.00 2.00 19.00 2.00

37.30 67.00 8.09 4.94 117.00 90.00 24.00 510.00 100.00 92.00 62.00

98.4 41.3 10.9 97.3 33.2 176.4 51.4 241.9 61.4 39.2 55.7

10.2 10.5 3.1 5.9 16.7 6.9 32.5 10.5 7.7 12.5

Mean

Biological methods Mineralization potential Nitrification potential Arginine deamininase Protease Basal respiration rate Biomass C Dehydrogenase Alkaline phospbatase Arylsulfatase Extractable organic C Extractable color index Invertase Conventionalmethods

%

*Number of measurements. tRaw = coefficient of variation for untransformed

data; Transformation = coefficient of variation following log transformation.

and type of rhizosphere activity in response to vegetation differences (Neal, 1982). Physical effects causing a reduction in arylsulfatase activity have been shown with arctic soils damaged by vehicles (Neal and Herbein, 1983), compacted skid trail soils (Dick et al., 1988) and in comparisons of conventional tillage vs no-till (Dick, 1984). Dehydrogenases are involved in respiratory reactions in viable cells and have often been proposed as indicators of microbial activity in soils (Skujins, 1973; Casida, 1977). Decrease in dehydrogenase as a result of disturbance has been reported in soils in logging areas, (Dick et al., 1988), in mine spoils and stored topsoil (Fresquez et al., 1987), in soil affected by processed oil shale (Sorensen et al., 1981) and in comparisons of cultivated vs no-till agriculture

(Doran, 1980; Dick, 1984; Dormaar and Lindwall, 1989). Disturbances causing reduction in oxygenation through compaction, poor drainage and excessive moisture could be expected to adversely affect microbial oxidase reactions. Although, in this study, dehydrogenase appears to be sensitive to a wide range of soil disturbances, there were no consistent relationships to other indicators of microbial activity such as biomass C, invertase or basal respiration rate. The effect of electrical conductivity seems to be related primarily to the presence of SO:- since the amount of salinity is significantly below that expected to cause large reductions in arylsulfatase or dehydrogenase activities (Frankenberger and Bingham, 1982).

Table 4. Coefficient loadings on each of the three different discriminant functions resulting from analysis of undisturbed (UD) and disturbed (D) soils

Biological variables. Four of the 12 biological vari-

Methods group

Variable

Biological only

Arysulfatase Dehydrogenase Basal respiration Extractable organic C

Coefficient 32.22 15.86 -2.53 -3.34

Conventional only

PH Organic C Sulfate-S Cation exchange capacity

33.01 6.68 1.76 -2.31

Biological + conventional

Electrical conductivity Extractable organic C Alkaline phosphatase Phosphate-P Cation exchange capacity Extractable color index Arginine deaminase

32.79 18.67 16.72 2.08 -4.66 -4.67 -6.00

Multivariate difSerences between disturbed and undisturbed soils

ables (arylsulfatase, dehydrogenase, extractable organic C and basal respiration) were retained by discriminant analysis (Table 4). The discriminant function (significant at P -C0.001) separating D from UD for this suite of variables is mainly defined by the variation in arylsulfatase and dehydrogenase. Soils having large values for these two enzyme activities result in positive canonical variate scores (Fig. 3; data value multiplied by a positive coefficient), thus are more likely to be grouped into the undisturbed group while high basal respiration and extractable organic C values more likely result in negative canonical scores (data value multiplied by a negative coefficient), being associated with disturbed soils. Strongest simple Pearson correlations between the

1508

M.J. ROWELL and L.Z. FLORENCE

canonical variate scores and the log-transformed data for these four variables were 0.72 (P < 0.001) for dehydrogenase and -0.49 (P < 0.001) for basal respiration. Conventional variables. Four of the 11 conventional methods (SO:-, pH, organic C and CEC) formed the significant (P < 0.001) discriminant function distinguishing disturbed and undisturbed soils (Table 4). Variation in pH was the overriding influence judged by its coefficient’s loading. Higher pH, organic C and SOi- resulted in larger positive canonical scores (Fig. 3) and greater likelihood of being classed as disturbed soils. Conversely, high CEC resulted in samples being placed in the undisturbed group. While pH had the largest loadin’g on the discriminant function, the highest significant correlation between the canonical variate scores and log data for any of these variables was for SOi- at 0.83 (P < 0.001). Soil pH and organic C also had significant positive correlations of 0.39 and 0.41 respectively. Individual data for CEC were not significant or negatively correlated with the canonical scores. The only significant negative correlation among the four variables in this conventional suite was higher organic C being associated with soils having lower pH. Such anomalies are the basis for utilizing the covariation among multiple soil assays via multivariate statistical methods. Biological and conventional variables. Upon combining both sets of variables from biological and conventional methods, electrical conductivity (Table 4) became the dominant loading on the discriminant function (significant at P < 0.001) separating disturbed and undisturbed soils. The influence of electrical conductivity was confirmed by it and the canonical variate having a simple Pearson correlation coefficient of 0.74 (P < O.OOl), thus supporting the conclusion that large positive canonical variate scores

were associated with high electrical conductivity values indicative of samples taken from disturbed soils (Fig. 3). Undisturbed samples then, were best defined by higher CEC, extractable color index and arginine deaminase. Each time variables are added or deleted from a multivariate analysis, the covariation structure is altered setting up a new matrix of interrelationships. Therefore, combining the conventional and biological variables did not result in a discriminant function containing necessarily the same loadings or variables as when the two suites were analyzed separately (Table 4). Hence, electrical conductivity was found to be very important in the combined set but not when using only the conventional methods group. Only extractable organic C and cation exchange capacity were found common to the other analyses. This may suggest an important link between soil organic matter maturity and the biological variables identified in this combined suite. Classification summary. The lowest, unbiased “actual error rate” (Johnson and Wichern, 1992) resulted when the biological and conventional suites were combined; 86 and 70% of the undisturbed and disturbed soil samples, respectively, were correctly classified following discriminant analysis (Table 5). This, of course, mainly results from the greater information contained on the discriminant function having a larger number of variables. Heterogeneity in misclassifying soil samples among the great soil groups (Table 5), within type of disturbance, was due largely to the less predictable variability among the Dark Brown and Brown Chernozemic soils; overall, 28 of 42 (67%) of the misclassifications were in these groups. Luvisolic soils, regardless of whether they were disturbed or undisturbed, were correctly placed into the proper group. Most misclassifications in the Chernozemic group were for

1

81 t! 8 Go I! 6 -' 0 P q

-2

UD EWogiial

D

UD

D

Conventional

UD

D

Bio+Conv

Fig. 3. Separation of canonical variate scores between industrially-disturbed (D) and undisturbed (UD) soils by canonical discriminant analysis. Refer to Table 4 which describes the variables contained in biological, conventional and biological + conventional analyses.

Characteristics associated with disturbed soils

1509 done between disturbed (D) and

Table 5. Classification summaries for all discriminant analyses where the grouping was

undisturbed (UD) soils Numbers misclassified* Date Set

Number

Type

Percentape uxxct

Biological

29

Conventional

25 32 36

Biological + conventional

28

UD D UD D UD D

76 68 91 64 86 70

23

AERt Bk

0 1 0 2 I 2

Db

2 5 1 10 0 3

B

Lv

Br

(So)

1 0 3 28 2 0 0 1 0 1 24 0 0 I 2 0 1 22 1 0 I Chemozem; Lv = Luvisol; Br =

*Soil groups: Bk = Black Chemozem; Db = Dark Brown Chemozem; B = Brown Brunisol. TAER: actual error rate (see Johnson and Wichem, 1992). In all these analyses, AER is less than what would be expected, given prior probabilities of UD = D = 0.5, at a significancelevel of 0.025 or less.

disturbed rather than undisturbed samples. This finding emphasizes that it requires more effort and skill to restore a fertile soil to its former productive state than a poorer soil to a previously marginal condition. Variability due to the disturbed-undisturbed factor and small, unequal sample sizes, argued against further analysis that might differentiate among the soil groups. Conclusions about the use of soil analysis in disturbed and reclaimed soils

An ability to predict the effect of disturbance and the success of soil rehabilitation practices is central to improved management of disturbed land. An ability to distinguish between broad soil groups and disturbance does not necessarily achieve this goal. Soil structure, aeration, water holding and nutrition, which are the cornerstones of soil fertility, are important discriminators of soil type as well as disturbance. The types of disturbances and their influences on the soil vary widely. The diagnostic variables for identifying soil disturbance (SO:-, arylsulfatase, dehydrogenase and electrical conductivity) revealed in our study may be of only secondary importance to fertility of a soil until their extreme values imbalance salinity and plant nutrient supply. More valuable information often comes from direct comparisons of disturbed and undisturbed soils at the same site. This does not mean that a more complex and interactive system would not work. The outcome of this study is important because it signifies that a basis now exists for confidence to be placed on choosing seven, of the original 23 soil assays, which have high potential for: (i) correctly reclassifying a once-disturbed soil as having been remediated sufficiently to be equivalent to an undisturbed soil, and (ii) conversely, reclassifying previously undisturbed soils that have been subject to industrial disturbance to ascertain whether the magnitude of disturbances are comparable to ones sampled in this study. Acknowledgements-M. J. Rowe11 thanks the Alberta Environmental Research Trust for financial support. The use of laboratory facilities and technical assistance at the Alberta Environmental Centre is gratefully acknowledged. We wish to thank Luscar Ltd, Suncor Inc., Norwest Soil Research Ltd, the Department of Soil Science, University of

Alberta and Alberta Environment for help in location of sampling sites; and P. Henry and J. Kirtz of the Alberta Environmental Centre for statistical computing and graphics. We thank Mr R. Johnson and Dr W. B. McGill for review and comments on the manuscript and Dr J. D. Jobson and Hai Van Nguyen for discussions on statistical methods.

REFERENCES Agriculture Canada Expert Committee on Soil Survey (1987) The Canadian System of Soil ClassQication, 2nd Edn. Agriculture Canada Publication 1646, Ottawa. Alef K. and Kleiner D. (1986) Arginine ammonification, a simple method to estimate the microbial activity potential in soils. Soil Biology & Biochemistry 18, 233-235. Anderson D. W., Paul E. A. and St Amaud R. J. (1974) Extraction and characterization of humus with reference clay-associated humus. Canadian Journal of Soil Science 54, 317-323.

Bentham H., Harris J. A., Birch P. and Short K. C. (1992) Habitat classification and soil restoration assessment using analysis of soil microbiological and physicochemical characteristics. Journal of Applied Ecology 29, 71 l-718.

Bums R. G. (Ed.) (1978) Soil Enzymes. Academic Press, London. Casida L. E. (1977) Metabolic activity in soil as measured by dehydrogenase determinations. Applied and Environmental Microbiology

34, 63&636.

Chandramohan D., Dcvandran K. and Natarajan R. (1974) Arylsulfatase activity in marine sediments. Marine Biology 27, 89-92.

Cooper P. J. M. (1972) Aryl sulphatase activity in northern Nigerian soils. Soil Biology & Biochemistry 4, 333-337. DeBolt D. C. (1974) A high sample volume procedure for the calorimetric determination of soil organic matter. Communications in Soil Science and Plant Analysis 5, 131-137.

Dick W. A. (1984) Influence of long-term tillage and crop rotation combinations on soil enzyme activities. Soil Science Society of America Journal 48, 569-574.

Dick R. P., Myrold D. D. and Kerle E. A. (1988) Microbial biomass and soil enzyme activities in compacted and rehabilitated skid trail soils. Soil Science Society of America Journal 52, 5 12-5 16.

Dodgson K. S., White G. F. and Fitzgerald J. W. (1982) Sulfatases of Microbial Origin, Vol. 2. CRC Press, Boca Raton. Doelman P. and Haanstra L. (1979) Effect of lead on soil respiration and dehydrogenase activity. Soil Biology & Biochemistry

11, 475-479.

Doran J. W. (1980) Soil microbial andbiochemical changes associated with reduced tillage. Soil Science Society of America Journal 44, 765-77 I.

M. J. ROWELLand L. Z. FLORENCE

1510

Dormaar J. F. and Lindwall C. W. (1989) Chemical differences in dark brown chernozemic Ap horizons under various conservation tillage systems. Canadian Journal of Soil Science 69, 481488. Flannery R. L. and Markus D. K. (1969) Simultaneous determination of phosphorus, calcium, potassium and magnesium in soil by autoanalyzer. Advances in Aufomated Analysis 2, 29-37. Technicon Industrial Method 237-72A, 1976. Frankenberger W. T. and Bingham F. T. (1982) Influence of salinity on soil enzyme activities. SoilScience Society of America Journal 46. 1171-l 177.

Fresquez P. R., Aldon E. F. and Lindemann W. C. (1987) Enzyme activities in reclaimed coal mine spoils and soils. La&cape

Urban Planning 14. 359-368.

Ganeshamurthy A. N. andNieisen N. E. (1990) Arylsulphatase and the biochemical mineralization of soil organic sulphur. Soil Biology & Biochemistry 22, 1163-l 165.

Gupta V. V. S. R., Lawrence J. R. and Germida J. J. (1988) Environmental impacts of elemental sulfur fertilization: 1. Effects on microbial biomass and enzyme activities in soils. Canadian Journal of Soil Science 68, 463473. Han K. W. and Yoshida T. (1982) Sulfur mineralization in rhizosphere of lowland rice. Soil Science and Plant Nutrition 28, 379-387.

Hershman I. E. and Temple K. L. (1979) Comparison of ATP, phosphatase, pectinolase, and respiration as indicators microbial activity in reclaimed coal strip mine spoils. Soil Science 127, 7&73. Jarvis B. W., Lang G. E. and Wieder R. K. (1987) Arylsulphatase activity in peat exposed to acid precipitation. Soil Biology & Bio>het&try 19, 107-109. Jenkinson D. S. and Ladd J. N. (1981) Microbial biomass in soil: measurement and turnover. In Soil Biochemistry (E. A. Paul and J. N. Ladd, Eds), Vol. 5. pp. 415471. Dekker, New York. Jobson J. D. (1991) Applied Multivariate Data Analysis, Vol. 1. Regression and Experimental Design. Springer, New York. Johnson R. A. and Wichern D. W. (1992) Applied Multivariate Statistical Analysis, 3rd Edn. PrentimHall, New Jersey. Kowalenko C. G. and Lowe L. E. (1975) Mineralization of sulfur from four soils and its relationship to soil carbon, nitrogen and phosphorus. Canadian Journal of Soil Science 55, 9-14.

Lachenbruch P. A. and Mickey M. R. (1968) Estimation of error rates in discriminant analysis. Technometrics 10, 1-11. Ladd J. N. and Butler J. H. A. (1972) Short-term assays of soil proteolytic enzyme activities using proteins and dipeptide derivatives as substrates. Soil Biology & Biochemistry 4, 19-30.

Lindemann W. C., Fresquez P. R. and Cardenas M. (1989) Nitrogen mineralization in coal mine spoil and topsoil. Biology and Fertility of Soils 7, 318-324.

Lowe L. E., Scagel A. M. and Klinka K. (1987) Chemical properties and classification of organic horizons from selected soils in British Columbia. Canadian Journal of Soil Science 67, 383-394.

Malhi H. H. and McGill W. B. (1982) Nitrification in three Alberta soils: effect of temperature, moisture and substrate concentration. Soil Biology & Biochemistry 14, 393-399.

Markus D. K., McKinnon J. P. and Buccafuri A. F. (1982) Automated analysis of nitrite, nitrate and ammonium nitrogen in soils: New Jersey Agricultural Experimental Station Publication 15117-84. Technicon Method No. 763-85(GT)A, 1985. McGill W. B., Dormaar J. F. and Rein]-Dwyer E. (1988) New perspectives on soil organic matter quality and dynamics on the Canadian Prairies. In Proceedings of the

Canadian Soil Science Society Symposium, Land Degradation: Assessmen and Insight inro a Western Canadian Problem (J. T. Harapiak, Ed.), pp. 3@48. Calgary,

Canada. McKeague J. A. (Ed.) (1978) Manual for Soil Sampling and Methods of Analysis. Canadian Society of Soil Science, Ottawa. Neal J. T. (1982) Abiontic enzymes in arctic soils: influence of predominant vegetation on phosphomonoesterase and sulphatase activity. Communications in Soil Science and Plant Analysis

131 863-878.

Neal J. T. and Herbein S. A. (1983) Abiontic enzymes in arctic soils: changes in suiphatase activity following vehicle disturbance. Pfanr and Soil 70, 4233427. Nolin M. C., Wang C. and Caillier M. J. (1989) Fertility grouping of Montreal Lowlands soil mapping units based on selected soil characteristics of the plow layer. Canadian Journal of Soil Science 69, 525-541.

Press M. C., Henderson J. and Lee J. A. (1985) Arysulphatase activities in peat in relation to acid deposition. Soil Biology & Biochemistry

17, 99-103.

Pulford I. D. and Tabatabai M. A. (1988) Effect of waterlogging on enzyme activities in soils. Soil Biology & Biochemistry

20, 2 15-2 19.

Roberts T. L., Bettany J. R. and Stewart J. W. B. (1989) A Hierarchical approach to the study of organic C, N, P, and S in western Canadian soils. Canadian Journal of Soil Science 69, 739-749.

Ross D. J. (1966) A survey of activities of enzymes hydrolysing sucrose and starch in soils under pastures. Journal of Soil Science 17, l-1 5. Ross D. J., Speir T. W., Cowling J. C. and Whale K. N. (1984) Temporal fluctuations in biochemical properties of soil under nasture: II. Nitrogen mineralization and enzyme activities. Australian J&ma1 of Soil Research 22, 319-330. Ross D. J., Speir T. W., Giltrap D. J., McNeilly B. A. and Molloy L. F. (1975) A principal component analysis of some biochemical activities in a climosequence of soils. Soil Biology & Biochemistry

7, 349-355.

Ross D. J., Speir T. W., Tate K. R., Cairns K. F. and Pansier E. A. (1982) Restoration of pasture after topsoil removal: effects on soil carbon and nitrogen minerahzation, microbial biomass and enzyme activities. Soil Biology & Biochemistry

14, 575-581.

Sarathchandra S. U., Perrott K. W. and Upsdell M. P. (1984) Microbial and biochemical characteristics of a range of New Zealand soils under established pasture. Soil Biology & Biochemistry 16, 177-183. Shields J. A., Paul E. A., St Arnaud R. J. and Head W. K. (1968) Spectroscopic measurement of soil color and its relationship to moisture and organic matter. Canadian Journal of Soil Science 48, 271-280.

Sims J. R. and Haby V. A. (1971) Calorimetric determination of soil organic matter. Soil Science 112, 137-141. Skiba U. and Wainwright M. (1983) Assay and properties of some sulphur enzymes in coastal sands. P/ant and Soil 70, 125132.

Skujins J. (1973) Dehydrogenase: an indicator of biological activities in arid soils. Bulletin of Ecological Research Communications

(Stockholm)

17, 223-241.

Sorensen D. L., Klein D. A., Ruzzo W. J. and Hersman L. E. (1981) Enzyme activities in revegetated surface soil overlying spent Paraho process oil shale. Journal of Environmental Quality 10, 369-37 1. Speir T. W. and Ross D. J. (1975) Effects of storage on the _activities of protease, urease, phosphatase, and sulphatase in three soils under pasture. New Zealand Journal of Science 18, 231-237.

_

Statistical Analysis Systems (1988) SAS/STAT Release 6.04. Statistical Analysis Systems Institute Inc., SAS Campus Drive, Cary, N.C. Statistical Analysis Systems (1991)

Characteristics associated with disturbed soils SAS/JMP Version 2.0. Statistical Analysis Systems Institute Inc., SAS Campus Drive, Cary, N.C. Stott D. E. and Hagedorn C. (1980) Interrelations between selected soil characteristics and arylsulfatase and urease activities. Communicarions in Soil Science and Plant Analysis

11, 935-955.

Stroo H. F. and Jenks E. M. (1982) Enzymatic activities and respiration in mine soils. Soil Science Society of America Journal 46, 548-553.

Tabatabai M. A. and Bremner J. M. (1969) Use of p-nitrophenyl phosphate for assay of soil phosphatase activity. Soil Biology & Biochemistry 1, 301-307. Tabatabai M. A. and Bremner J. M. (1970) Arylsulfatase activity of soils. Soil Science Socieiy of America Proceedings 34, 225-229.

Tabatabai M. A. and Bremner J. M. (1972) The distribution of total and available sulfur in selected soils and soil profiles. Agronomy Journal 64, 4&44.

1511

Tyler G. (1976) Heavy metal pollution, phosphatase phosactivity, and mineralization of organic phorus in forest soils. Soil Biology & Biochemistry 8, 327-332.

Vekemans X., Godden B. and Penninckx M. J. (1989) Factor analysis of the relationships between several physico-chemical and microbiological characteristics of some Belgian agricultural soils. Soil Biology & Biochemistry 21, 53-58.

Wainwright M. (1981) Enzyme activity in intertidal sands and salt-marsh soils. P/ant and Soil 59, 357-363. Waring S. A. and Bremner J. M. (1964) Ammonium production in soil under waterlogged conditions as an index of nitrogen availability. Nature 201, 951-952. Will M. E., Graetz D. A. and Roof B. S. (1986) Effect of simulated acid precipitation on soil microbial activity in a Typic Quartzipsamment. Journal of Environmental Quality 15, 399403.