Lead sorption to selected Portuguese soils

We studied the interaction of lead with seven Portuguese soils with different physical and chemical properties in order to elucidate more fully the behaviour of Pb in soil. We studied these adsorption phenomena by voltammetric titrations with differential pulse polarography (DPP) at different pH (6.0–7.2) and ionic strengths, I (0.010–0.50 m ) in order to clarify some of the factors that might control soil sorption capacity for Pb. From the voltammetric data, average formation constants, , and binding capacity, C_c, have been estimated according to a surface complexation model based on Scatchard and van den Berg–R?zic methods. Linear Scatchard and van den Berg–R?zic plots (r≥ 0.99) indicated that the results can be interpreted according to the existence of just one predominant active site for Pb(II) adsorption. The values from both procedures () agreed in all cases (r= 0.938, n= 66, P < 0.001). The same happened with C_c values that were statistically equivalent (r= 0.9998; n= 66; P < 0.001). The C_c values were found to depend on the pH and I, as well as on the soil properties. Either Langmuir or Freundlich isotherms fitted the experimental data well (r > 0.90, P < 0.05). The lead binding capacities were strongly and significantly correlated (P < 0.05) with pH, cation exchange capacity, organic carbon, loss‐on‐ignition, total Al₂O₃ content, extractable forms of Al and pyrophosphate extractable Fe, [Fe_p]. From a forward, stepwise regression model we concluded that [Al₂O₃], [Pb′] (concentration of labile lead in solution), [Fe_p], pH and I are able to explain more than 99.7% of the variation in lead sorption in our soils. The soils’ surface groups with special affinity to Pb(II) are in the inorganic fraction associated with aluminium.     

THE VOLUME AND POROSITY OF SOIL CRUMBS

A simple and inexpensive apparatus (a test-tube, burette, and pin) is described for measuring volumes by liquid displacement to an accuracy of greater than 0.5 per cent. This has been adapted to measure soil crumb porosities, ε_c, by saturating 3–4 g samples of crumbs with kerosene, measuring the weight of kerosene retained internally, then measuring their volume by displacement. Three estimates of crumb porosity from these measurements are compared. Experimental values range from ε_c= 0.205 for the headland of an arable field to ε_c= 0.351 for a permanent pasture. Crumb porosity is proposed as a measure of structural status for soils because it assesses the degree to which soil management has succeeded in holding the constituent primary particles apart from the positions of inherent closest packing that they would ultimately assume in an unstable soil. By comparison, the inter-crumb porosity, ε_v, can be used as a measure of cultivation status. In the form expressed, these two porosities are related to the more frequently encountered total porosity ε_t by the relation      

Humic substances in acid organic soils: modelling their release to the soil solution in terms of humic charge

Batch titration experiments were carried out with organic soil samples in order to investigate the release to the solution phase of humic substances (HS). Measurements were made of pH, dissolved organic carbon (DOC) concentration, and the concentration of mono-meric (inorganic + organic) aluminium, as functions of added acid or base. DOC was taken to be entirely due to HS. The results can be interpreted in terms of a model in which the soil is considered to contain two types of HS–mobile or potentially mobile (HSM), and immobile (HSI). The binding of inorganic ions by the HS is calculated using humic ion-binding model IV, previously developed in this laboratory. Model IV allows the charges on the HS (Z_HSM, Z_HST) to be calculated; these are determined mainly by the binding of H+ and A13+. Concentrations of HS in solution, [HS_aq], are given by the equation: where |Z_HSM| is the modulus of Z_HSM, n_HSM is the carboxyl group content of HSM, c_HSM is the soil content of HSM, β is a fitting parameter, and square brackets, [ ], indicate concentrations. For most of the soils a value for β of 3 gives acceptable agreements between measured and calculated values of [HS_aq], indicating a major influence of charge on release. The optimized value of c_HSM differs considerably among soils, whereas c_HIS varies by only a factor of about two. Total humic contents (c_HSM+ c_HSI estimated by model optimization are in approximate agreement with values estimated by extraction of the soils with NaOH.     

KINETICS OF ISOTOPIC EXCHANGE OF PHOSPHATE ADSORBED ON GIBBSITE

An Elovich-type equation has been used to describe the kinetics of isotopic exchange of phosphate adsorbed on the surface of gibbsite. The equation is where A and B are parameters; θ=bF/[b+a(I-F]; a and b are the molar concentrations of the phosphate on the crystal surface and in solution respectively and F is the fraction exchange of the radio-isotope at time t. First-order rate constants were obtained from the equation. The reference state for the first-order rate constants, and the distribution of activation energies, for exchange can be related to the Elovich equation parameters. The kinetic results are consistent with S_N1 dissociation or S_N2 bimolecular solvolysis for the phosphate ligand. The eschange reaction is subject to an acid-base catalysis the exact nature of which could not be determined from the available data.     

Determination of organic carbon and nitrogen in particulate organic matter and particle size fractions of Brookston clay loam soil using infrared spectroscopy

The objective of this study was to determine whether models developed from infrared spectroscopy could be used to estimate organic carbon (C) content, total nitrogen (N) content and the C:N ratio in the particulate organic matter (POM) and particle size fraction samples of Brookston clay loam. The POM model was developed with 165 samples, and the particle size fraction models were developed using 221 samples. Soil organic C and total N contents in the POM and particle size fractions (sand, 2000–53 µm; silt, 53–2 µm; clay, <2 µm) were determined by using dry combustion techniques. The bulk soil samples were scanned from 4000 to 400 cm^?1 for mid‐infrared (MIR) spectra and from 8000 to 4000 cm^?1 for near‐infrared (NIR) spectra. Partial least squares regression (PLSR) analysis and the ‘leave‐one‐out' cross‐validation procedure were used for the model calibration and validation. Organic C and N content and C:N ratio in the POM were well predicted with both MIR‐ and NIR‐PLSR models ( = 0.84–0.92; = 0.78–0.87). The predictions of organic C content in soil particle size fractions were also very good for the model calibration ( = 0.84–0.94 for MIR and = 0.86–0.92 for NIR) and model validation ( = 0.79–0.94 for MIR and = 0.84–0.91 for NIR). The prediction of MIR‐ and NIR‐PLSR models for the N content and the C:N ratio in the sand and clay fractions was also satisfactory ( = 0.73–0.88; = 0.67–0.85). However, the predictions for the N content and C:N ratio in the silt fraction were poor ( = 0.23–0.55; = 0.20–0.40). The results indicate that both MIR and NIR methods can be used as alternative methods for estimating organic C and total N in the POM and particle size fractions of soil samples. However, the NIR model is better for estimating organic C and N in POM and sand fractions than the MIR model, whereas the MIR model is superior to the NIR model for estimating organic C in silt and clay fractions and N in clay fractions.     

THE MEASUREMENT AND MECHANISM OF ION DIFFUSION IN SOILS. VIII —A THEORY FOR THE PROPAGATION OF CHANGES OF pH IN SOILS

The way pH changes in soil are propagated by movement of acids and bases is described. In acid soils the H₃O⁺-H₂O acid-base pair is most important, while in alkaline soils the H₂CO₃-HCO₃^? pair is always dominant, its effect depending directly on the pressure of CO₂. In neutral and slightly acid soils, soluble organic matter and the H₂PO₄^?-HPO²₄^? pair may also contribute. A soil acidity diffusion coefficient is derived, and defined as: where v_l= the volume fraction of the soil solution, f_l= the impedance factor for the liquid diffusion pathway, b_HS= the pH buffer capacity of the soil, b _HB= the pH buffer capacity of each mobile acid-base pair, Dl _HB= the diffusion coefficient of each mobile acid-base pair in free solution, and the sum is taken over all mobile acid-base pairs. The soil acidity diffusion coefficient may be used to predict the course of pH equilibration in practical situations. It is high in acid and alkaline soil, and at a minimum in slightly acid soil. It is little affected by variation of the ionic strength of the soil solution at concentrations less than 0.01M. When the pH buffer capacity of the soil is constant, and only the H₃O⁺-H₂O and H₂CO₃-HCO₃^? pairs are important, the soil acidity diffusion coefficient varies as cosh{2.303(pH—pH₀)}, where pH₀ is the pH at which the soil-acidity diffusion coefficient is a minimum.     

THE MEASUREMENT AND MECHANISM OF ION DIFFUSION IN SOIL

If an exchangeable ion in soil diffuses along a liquid and solid pathway, its diffusion coefficient may be expressed as where D, v, f, C are diffusion coefficient, volume fraction, impedance factor, and concentration terms and the suffixes _l,_S refer to liquid and solid. The self-diffusion coefficient of the ion is then where D′, D′_t, and D′_s, are self-diffusion coefficients. D and D′ will vary with concentration. In diffusion out of the soil to a zero sink, the appropriate average diffusion coefficient is, approximately, the self-diffusion coefficient in the undisturbed soil. Diffusion of one ion species is influenced by other ions diffusing in the system through the diffusion potential set up. When ions are diffusing to plant roots, the diffusion potential is likely to be small. A more likely, though more complicated, expression for D than the first equation above is derived by assuming the ion to follow solid and liquid pathways in series as well as in parallel.     

A comparison of the convection-dispersion equation and transfer function model for predicting chloride leaching through an undisturbed, structured clay soil

The transfer function mode) (TFM) and convection-dispersion equation (CDE) were compared for predicting Cl ^? transport through a calcareous pelosol during steady, nearsaturated water flow. Large, undisturbed soil cores were used at constant irrigation intensities (q₀) between 0.3 and 3 cm h^?1, with a step-change in Cl^? concentration. The assumption of a lognormal distribution of travel times–characterized by the mean (μ) and variance (σ²)–permitted the flux-averaged breakthrough curves (BTCs) to be modelled very accurately by the TFM. The BTCs could be modelled equally well by the CDE when both the mean pore water velocity (v) and dispersion coefficient (D) were optimized simultaneously by the method of least squares, but not when v was put equal to q₀/_v, where _V was the mean volumetric water content. The best estimate of v was consistently > q₀/_v, which suggested that not all the pore water was effective in chloride transport. An operationally defined transport volume (θ_st) was calculated from the mean () or median (τ_m) travel times derived from the TFM. Chloride exclusion was not solely responsible for θ_st() being <_V: immobile water also contributed. The positive skewness of the travel time distributions meant that θ_st(τ_m) < θ_st(), indicating the effectiveness of macropore flow in solute transport. Dαv^1.42 (from the CDE), and σ²αv (from the TFM), confirmed that Cl^? dispersion increased as flow velocity increased. Flux-averaged concentrations were used to calculate the volume-averaged resident concentrations. They matched the measured Cl^? concentrations most closely when there was a gradual decrease in measured Cl ^? concentration with depth, but not when Cl ^? decreased sharply below c. 10 cm. Calculations assuming that all the water was effective in chloride transport gave less accurate results. Comparison of the measured and predicted concentrations of solute demonstrated that this must be a critical part of the evaluation of any model of solute transport.     

Determination of total soil organic C and hot water‐extractable C from VIS‐NIR soil reflectance with partial least squares regression and spectral feature selection techniques

The calibration of soil organic C (SOC) and hot water‐extractable C (HWE‐C) from visible and near‐infrared soil reflectance spectra is hindered by the complex spectral interaction of soil chromophores that usually varies from one soil or soil type to another. The exploitation of spectral variables from spectroradiometer data is further affected by multicollinearity and noise. In this study, a set of soil samples (Fluvisols, Podzols, Cambisols and Chernozems; n = 48) representing a wide range of properties was analysed. Spectral readings with a fibre‐optics visible to near‐infrared instrument were used to estimate SOC and HWE‐C contents by partial least squares regression (PLS). In addition to full‐spectrum PLS, spectral feature selection techniques were applied with PLS (uninformative variable elimination, UVE‐PLS, and a genetic algorithm, GA‐PLS). On the basis of normalized spectra (mean centring + vector normalization), the order of prediction accuracy was GA‐PLS ? UVE‐PLS > PLS for SOC; for HWE‐C, it was GA‐PLS > UVE‐PLS, PLS. With GA‐PLS, acceptable cross‐validated (cv) prediction accuracies were obtained for the complete dataset (SOC, , RPD_cv = 2.42; HWE‐C_cv, , RPD_cv = 2.13). Splitting the soil data into two groups with different basic properties (Podzols compared with Fluvisols/Cambisols; n = 21 and n = 23, respectively) improved SOC predictions with GA‐PLS distinctly (Podzols, , RPD_cv = 3.14; Fluvisols/Cambisols, , RPD_cv = 3.64). This demonstrates the importance of using stratified models for successful quantitative approaches after an initial rough screening. GA selection frequencies suggest that the spectral region over 1900 nm, and in particular the hydroxyl band at 2200 nm are of great importance for the spectral prediction of both SOC and HWE‐C.     

THE RESPONSE OF UNSATURATED SOILS TO ISOTROPIC STRESS

Soil compression is caused in agriculture by tillage implements, plant roots, treading by animals, and by wheels and tracks of vehicles. Increases in soil density resulting from compression usually reduce crop growth and yield. Compression and expansion of samples of five remoulded soils, each at several moisture contents, were investigated. Soil samples were subjected to isotropic stress of up to 3.5 MN m^?2 in a pressure cell. Volume changes were measured by the volume of pore fluid effused or infused through one of the sample surfaces. Particle packing densities, D, were well described by the equation where D₀ is the maximum limiting value of D, P is the applied isotropic stress, and B, C, K, L are adjustable parameters. One of the exponential terms in this equation describes deformation of soil crumbs and the other describes rearrangement of individual particles. Two sample sizes gave similar values for the equation parameters. A small increase in moisture content results in a large increase in soil compressibility. It is hypothesized that resistance to compression may be one of the principal influences in the mechanical restriction of root growth.     

QUALITY CONTROL IN SOIL SURVEY

The paper examines 66 Australian soil surveys in a variety of terrains (but not close forest), by several survey procedures, and published at a range of map scales. It relates the Survey Effort (E) of professional staff (in man-days per km²) to (I) survey procedure, (2) the kind of mapping unit, and (3) the intricacy of the soil pattern mapped. Intricacy (I), the average number of mapped soil boundaries crossed by 1 km of random linear traverse, is related to the total length of mapped boundary (km per km²). When the surveys are grouped according to survey procedure and mapping unit, the survey effort for each group may be described by a regression of the form . B could not be shown to differ significantly between groups. D varied in the ratio 0.5: I, according to whether or not surveys used air photograph interpretation, and in the ratio 0.3:0.7:1.o, according to whether they mapped land systems, other compound mapping units, or simple mapping units. Since the choice of survey procedure and mapping unit is usually governed by the intricacy of the soil pattern the effect of these factors can be summarized in a single regression for all 66 surveys: There is a significant (P≤ 0.001) log-log regression between I and map scale.     

Evaluating soil sodium indices in soils of volcanic nature conducive or suppressive to Fusarium wilt of banana

The ability of three soil Na indices to predict soil conduciveness or suppressiveness to disease caused by the soil fungus Fusarium oxysporum f. sp. cubense was evaluated in seven banana plantations from the Canary Islands (Spain). These indices were exchangeable sodium percentage (ESP), soluble Na (SS₀) and sodium adsorption ratio (SAR₀) in 1:2.5 soil-water extracts (SAR_w and total cationic concentration (TCC_w)=0. Sodium selectivity coefficients (K_G0,K₀) and TCC₀ were calculated from soil exchange and solution data. The effects of ESP, SAR₀, SS₀, TCC₀, K_G0 and K₀ on soil available iron (Fe extracted from soil by DTPA) and aggregate stability in water (water-stable aggregates (WSA), 200-2000 μm) were also studied. Our results showed that SAR₀ calculated using cationic concentrations in 1:2.5 extracts might be a good indication of a relationship between SS₀ and soluble divalent cations in conducive and suppressive volcanic soils to Fusarium. Both TCC₀ and dispersion-flocculation concentrations seem to be not linked to soil suppressiveness or conduciveness to Fusarium wilt. These results suggested that soil physical properties seem to be not controlled by Na behaviour in these type of soils and, therefore, sodicity and salinity should not be a problem from a physical point of view. Moreover, SS₀ and SAR₀ were always greater in suppressive areas than in conducive areas. SAR₀ was significantly correlated with SS₀ but correlations between ESP against SS₀ and SAR₀ were weak. For SAR₀ values above 2.5 (mmol_c l⁻¹)^1/2 and ESP values below 15%, the exchangeable Na did not seem to be related to the capacity of suppressive areas to release more Na to soil solution. Larger values of SS₀ were observed in suppressive areas for these values of SAR₀ and ESP. It implies a lower quantity of soluble Na salts in conducive samples. A high Na salt content in soil can produce an increase of soil pH, which exerts a negative influence on available Fe release to soil solution. A clear separation between conducive and suppressive samples from relations between SS₀ and SAR₀ against WSA and Fe-DTPA showed that SS₀ and SAR₀ can be satisfactory indices to study the influence of Na concentrations on the incidence of Fusarium wilt. The mass of WSA increase in conducive areas might be favoured by the smaller amounts of soil solution Na found in these samples. In conclusion, our data provide evidence that release of Na to soil solution could favour soil suppressiveness to Fusarium wilt limiting soil aggregation and the availability of Fe, at least in soils of volcanic nature that are not affected by salinity or sodicity processes.     

A simple equation to describe sorption of anions by goethite

A simple equation to describe sorption of anions by goethite would be useful as a means of characterizing batches of goethite and in studies of plant uptake of anions from the sorbed form. A suitable relationship between solution concentration (c) of phosphate or citrate and their sorption (S) by goethite at a constant pH or at different pH values is where b is a parameter, SMax is the maximum sorption, and a is a parameter at constant pH. In the middle range of sorption (from about 30% to about 70% of maximum sorption) this equation approximated to a Tempkin equation, but the full equation is more useful as it applied over the whole sorption range. The values of a varied with pH. This variation could be explained by changes in the electric potential of the adsorbing surfaces and in the degree of dissociation of the anions. The parameter a could therefore be replaced by a function of pH. The effects were consistent with formation of bidentate phosphate complexes and tridentate citrate complexes with the goethite surface.     

Suitability of a hydraulic‐conductivity model for predicting salt effects on swelling soils

Many empirical approaches have been developed to analyze changes in hydraulic conductivity due to concentration and composition of equilibrium solution. However, in swelling soils these approaches fail to perform satisfactorily, mainly due to the complex nature of clay minerals and soil–water interactions. The present study describes the changes in hydraulic conductivity of clay (Typic Haplustert) and clay‐loam (Vertic Haplustept) soils with change in electrolyte concentration (TEC) and sodium‐adsorption ratio (SAR) of equilibrium solution and assesses the suitability of a model developed by Russo and Bresler (1977) to describe the effects of mixed Na‐Ca‐Mg solutions on hydraulic conductivity. Four solutions encompassing two TEC levels viz., 5 and 50 mmol_c L^–1 and two SAR levels viz., 2.5 and 30 mmol^1/2 L^–1/2 were synthesized to equilibrate the soil samples using pure chloride salts of Ca, Mg, and Na at Ca : Mg = 2:1. Diluting 50 mmol_c L^–1 solution to 5 mmol_c L^–1 reduced saturated hydraulic conductivity of both soils by 66%, and increasing SAR from 2.5 to 30 mmol^1/2 L^–1/2 decreased saturated hydraulic conductivity by 82% and 79% in clay and clay‐loam soils, respectively. Near saturation, the magnitude of the change in unsaturated hydraulic conductivity due to the change in TEC and SAR was of 10³‐ and 10²‐fold, and at volumetric water content of 0.20 cm³ cm^–3, it was of 10¹⁴‐ and 10⁶‐fold in clay and clay‐loam soils, respectively. Differences between experimental and predicted values of saturated hydraulic conductivity ranged between 0.6% and 11% in clay and between 0.06% and 2.1% in clay‐loam soils. Difference between experimental and predicted values of unsaturated hydraulic conductivity widened with drying in both soils. Predicted values were in good agreement with the experimental values of hydraulic conductivity in clay and clay‐loam soils with R² values of 0.98 and 0.94, respectively. The model can be satisfactorily used to describe salt effects on hydraulic conductivity of swelling soils in arid and semiarid areas, where groundwater quality is poor.     

The solid solution equilibria of lead and cadmium in polluted soils

A method for the measurement of Pb and Cd in equilibrium soil solutions involving soil equilibration with a dilute Ca electrolyte, centrifugation and filtration to <0.2 μm was evaluated. The procedure was subsequently used for the analysis of 100 Pb- and 30 Cd-contaminated soils. Solutions were analysed for Pb- and Cd using graphite-furnace AAS and the concentrations of Pb²⁺ and Cd²⁺ were estimated using standard speciation calculations. The concentrations of Pb and Cd found in the soil solutions were in the range 3.5–3600 μg dmp ^?3 and 2.7–1278 μg dm ^?3 respectively; both ranges represented less than 0.1% of the total metal concentration in the soils. Depending on solution pH, Pb ⁺² accounted for between 42–78% of Pb in solution while about 65% of Cd in solution was present as Cd⁺². The concentrations of Pb²⁺ and Cd²⁺ in solution suggested that the soil solutions were undersaturated with respect to the solid phases PbC0₃ and CdC0₃ but supersaturated with respect to Pb₅(P0₄)₃Cl and, for some samples, Cd₃(P0₄)₂ respectively. However, for both metals, a good empirical relationship was obtained between the total metal concentration in soil (mol kg^?1), free metal concentration in solution (mol dm^?3) and solution pH. The relationships took the general form of a pH-dependent Freundlich adsorption equation: For both lead and cadmium relationships, the values ofn and K₁ were close to unity, so that the distribution coefficient could be estimated from pH and a single metal-dependent constant, K₂. The algorithms appeared to be valid over a metal concentration range of four logarithmic units and pH range of 3.5–7.5.     

A new method for the estimation of total dissolved salts in saturation extracts of soils from electrical conductivity

We have determined electrical conductivity (E_e) and total dissolved salts (S) in saturation extracts from 39 soil samples from the Baza basin (Province of Granada, south-east Spain). E_e ranged from 2.8 to 110dS m^?3, and S from 2 to 444 8 dm^?3. The relationship between S and E_e was not linear. When the saturation extracts were diluted with progressively larger quantities of distilled water and their electrical conductivity calculated (E_ec) with the equation where E_d and E_w are the conductivity of the diluted extract and the distilled water and f is the dilution factor, the relationship between S and E_ec tended to become linear. The highest linear correlation coefficient relating S (mg dm^?3) and E_ec (dS m^?1) was reached when E_ec, values were calculated for dilutions with a conductivity (E_d) between 0.1 and 0.3 dS m^?1 (E*_ec). The regression equation was S= 490 E*_ec with r²= 0.999. This relationship can be used in all saturation extracts, regardless of the concentration and type of ions present.     

The Burns leaching equation

The simplicity and utility of Burns' leaching equation make it worthy of study. The equation may be written as where X is the fraction of initially surface-resident fertilizer leached below depth z by net rainfall I, in soil with a volumetric water content at ‘field capacity’ of θ. The equation is analysed using transfer functions. The analysis shows that Burns' equation is consistent with an ‘independent flow tube’ soil leaching model, rather than the soil solution being well-mixed at each soil depth as Burns suggested. The flux and resident soil solution soil concentration profiles are shown to be quite different. An alternative definition of θ is suggested. The behaviour of ‘a Burns soil’ for different initial and boundary conditions is discussed.     

Dissolved organic nitrogen and carbon in pastoral soils: the New Zealand experience

Dissolved organic nitrogen (DON) and dissolved organic carbon (DOC) in soils are increasingly recognized as important components of nutrient cycling and biological processes in soil‐plant ecosystems. The aims of this study were to: (i) quantify the pools of DON and DOC in a range of New Zealand pastoral soils; (ii) compare the effects of land use changes on these pools; and (iii) examine the seasonal variability associated with these two components of dissolved organic matter. Soil samples (0–7.5 cm depth) from 93 pastoral sites located in Northland, Waikato, Bay of Plenty and Otago/Southland, New Zealand, were collected in autumn. Adjacent sites under long‐term arable cropping or native vegetation and forestry land use were also sampled at the same time to estimate the impacts of different land use on DON and DOC in these soils. Twelve dairy and 12 sheep and or beef pastures were sampled in winter, spring, summer and autumn for a 2‐year period to study the seasonal fluctuations of DON and DOC. A field incubation study was also carried out in a grazed pasture to examine fluctuations in the concentrations of and and DON levels in soil. Other soil biological properties, such as microbial biomass‐C, biomass‐N and mineralizable N, were also measured. Pastoral soils contained the greatest amounts of DON (13–93 mg N kg⁻¹ soil, equivalent to 8–55 kg N ha⁻¹) and DOC (73–718 mg C kg⁻¹ soil, equivalent to 44–431 kg C ha⁻¹), followed by cropping and native vegetation and forestry soils. The DON concentration in soils was found to be more seasonally variable than DOC. There was approximately 80% fluctuation in the concentration of DON in winter from the annual mean concentration of DON, while DOC fluctuated between 23 and 28% at the dairy and the sheep and beef monitoring sites. Similar fluctuations in the concentrations of DON were also observed in the field incubation studies. These results indicate that DON is a dynamic pool of N in soils. There was a strong and significant positive correlation between DON and DOC in pastoral soils (r = 0.71, P < 0.01). There were also significant positive correlations between DON and total soil C (r = 0.59, P < 0.01), total soil N (r = 0.62, P < 0.01) and mineralizable N (r = 0.47, P < 0.01). The rather poor correlations between total soil C and N with DOC and DON, suggest other biogeochemical processes may be influencing concentrations of DOC and DON in these soils. Given the size of DON and DOC pools in the pastoral soils, we suggest that these pools of C and N should be taken into account when assessing the impact of pastoral land use on soil C and N enrichment of surface and groundwater.     

Aluminosilicate phases during initial clay formation: H+-Al3+–oxalic acid–silicic acid–Na+ system

The solid phases and the precipitation boundary characterizing the system H⁺-Al³⁺-oxalic acid-silicic acid-Na⁺ are discussed. Model experiments have been used to throw more light on two environmental problems: the formation of sparingly soluble aluminium silicates in oceans and alkaline lakes, which could be determining aluminium and silicate concentrations in pore waters of sediments, and the validity of inorganic and organic mechanisms of podzolization and their significance for soil science. pH and Tyndallometric measurements were performed at constant ionic strength of 0.6 M NaCl at 25°C. Three phases Al(OH)₄, H₄SiO₄ (phase Vi^a), Al₂, (OH)₆.H₄SiO₄ (phase VI^b) and NaAl(OH)₄.(H₄SiO₄), (phase VIII) determine the precipitation boundary. Phase NaAl(OH)₄.H₄SiO₄ (phase VII precipitates at 0.4pH units above NaAl(OH)₄.(H₄SiO₄)₂. Using a set of previously determined binary and ternary complexes, and phases of the subsystems, the following formation constants were deduced: Phases VI^a and VI^b are described as end-members of the allophane series with Si: Al ratios of 1:1 and 1.2. Phase VI^b was identified with protoimogolite allophane. These two phases are good model clays for podzolic soils and are extremely soluble at pH < 4. Sodium phases could be hydrous feldspathoids. These phases are possible in sediments of seawater or saline lakes. It is suggested that organic and inorganic mechanisms of podzolization operate sequentially and that neither of them alone can completely describe the process.