methodology for the environmental assessment of the soil state and regulation of the soil quality

10
916 ISSN 1064-2293, Eurasian Soil Science, 2009, Vol. 42, No. 8, pp. 916–925. © Pleiades Publishing, Ltd., 2009. Original Russian Text © A.S. Yakovlev, V.M. Gendugov, G.P. Glazunov, M.V. Evdokimova, E.A. Shulakova, 2009, published in Pochvovedenie, 2009, No. 8, pp. 984–995. INTRODUCTION Along with the problems related to the classification of unfavorable loads on soils and environment and the selection of adequate response parameters (indices), the development of methods for the environmental assessment of the soil state faces problems in the response interpretation. These methods are usually based on the determination and mapping of the “dose– effect” response parameter using a “norm–pathology” quality schedule. It was shown [1, 2, 7] that the numer- ous methods of mapping the response parameter on a quality scale are based on two approaches. One of them uses a load scale arbitrarily graduated on the basis of theoretical concepts and empirical data for the response as a quality scale; the other introduces a separate qual- ity scale, on which the response to the load is mapped using the desirability function. Both approaches lack unique estimates because of the arbitrary selection of the estimation schedules and the desirability functions. Both approaches also face the problem of information compression (the acquisition of a united integrated esti- mate) under the simultaneous action of different loads, which is related to the consideration of (1) their mutual effect (additivity, antagonism, or synergism) and (2) their different dimensionalities. This work deals with the solution of the above prob- lems and describes methods for (1) introducing a soil state parameter into the load response functions, (2) introducing a norm–pathology soil quality schedule into the state parameter functions, (3) assessing the state of the soil under a set of loads, (4) using the pro- posed method for a set of loads, and (5) expanding the state parameter to other natural environments. The new approach is based on (1) the use of the responses of soil, soil biota, or test organisms to the load; (2) the use of three qualitative states of the ecosys- tem under the load (including two homeostatic states): (a) a background state with a high viability, (b) a dis- turbed state almost without viability, and (c) a transi- tional state with unstable viability); (3) the derivation of the functional of the soil state (a function of the load response function) within the conservation laws; (4) a supposition about the additivity of the loads; and (5) the presumption of the probabilistic nature of the soil state parameter and the use of the probability theory rules for the integrated assessment of the soil state under several loads (under the assumption of the absence of their incompatibility) and the responses to them. The soil state was assessed in three stages: (1) the analysis of the dose–effect response; (2) from its result, the calculation of the soil state parameter value; and (3), on its basis, the environmental assessment of the qual- ity of the soil (or another natural environment) using a five-point schedule. Conventional methods were used at the first stage, and the results of this work were used at the second and third stages. DERIVATION OF A SOIL STATE PARAMETER We proceed from the assumption that the soil state has its own measure and is characterized by some quan- titative parameters p at each time moment. Each state of the soil corresponds to multiple and various responses to the load, e.g., in the form of specific implementations of its ecological functions or the soil biota state. If the response is measurable, it can be quantitatively DEGRADATION, REHABILITATION, AND CONSERVATION OF SOILS Methodology for the Environmental Assessment of the Soil State and Regulation of the Soil Quality A. S. Yakovlev a , V. M. Gendugov b , G. P. Glazunov a , M. V. Evdokimova a , and E. A. Shulakova a a Faculty of Soil Science, Moscow State University, Moscow, 119992 Russia b Faculty of Mechanics and Mathematics, Moscow State University, Moscow, 119992 Russia E-mail: [email protected] Received June 10, 2008 Abstract—A method of determining the parameters of the soil state and assessing the quality of soils subject to anthropogenic loads was substantiated. An equation for the state function was derived in the general form, and a method was proposed for determining the equation parameters based on the interpretation of the experi- mental “dose–response” relationships. A schedule was developed for the environmental assessment of the soil quality. Procedures were substantiated and specified for assessing the state and quality of erosion-hazardous soils, the quality of the air with an unorganized dust source, the state and quality of the water on the basis of biotesting, the integrated assessment of the soil state under multiple loads, and the environmental–economic assessment of the quality of lands and waste disposal sites. DOI: 10.1134/S1064229309080109

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ISSN 1064-2293, Eurasian Soil Science, 2009, Vol. 42, No. 8, pp. 916–925. © Pleiades Publishing, Ltd., 2009.Original Russian Text © A.S. Yakovlev, V.M. Gendugov, G.P. Glazunov, M.V. Evdokimova, E.A. Shulakova, 2009, published in Pochvovedenie, 2009, No. 8, pp. 984–995.

INTRODUCTION

Along with the problems related to the classificationof unfavorable loads on soils and environment and theselection of adequate response parameters (indices),the development of methods for the environmentalassessment of the soil state faces problems in theresponse interpretation. These methods are usuallybased on the determination and mapping of the “dose–effect” response parameter using a “norm–pathology”quality schedule. It was shown [1, 2, 7] that the numer-ous methods of mapping the response parameter on aquality scale are based on two approaches. One of themuses a load scale arbitrarily graduated on the basis oftheoretical concepts and empirical data for the responseas a quality scale; the other introduces a separate qual-ity scale, on which the response to the load is mappedusing the desirability function. Both approaches lackunique estimates because of the arbitrary selection ofthe estimation schedules and the desirability functions.Both approaches also face the problem of informationcompression (the acquisition of a united integrated esti-mate) under the simultaneous action of different loads,which is related to the consideration of (1) their mutualeffect (additivity, antagonism, or synergism) and (2)their different dimensionalities.

This work deals with the solution of the above prob-lems and describes methods for (1) introducing a soilstate parameter into the load response functions, (2)introducing a norm–pathology soil quality scheduleinto the state parameter functions, (3) assessing thestate of the soil under a set of loads, (4) using the pro-posed method for a set of loads, and (5) expanding thestate parameter to other natural environments.

The new approach is based on (1) the use of theresponses of soil, soil biota, or test organisms to theload; (2) the use of three qualitative states of the ecosys-tem under the load (including two homeostatic states):(a) a background state with a high viability, (b) a dis-turbed state almost without viability, and (c) a transi-tional state with unstable viability); (3) the derivation ofthe functional of the soil state (a function of the loadresponse function) within the conservation laws; (4) asupposition about the additivity of the loads; and (5) thepresumption of the probabilistic nature of the soil stateparameter and the use of the probability theory rules forthe integrated assessment of the soil state under severalloads (under the assumption of the absence of theirincompatibility) and the responses to them.

The soil state was assessed in three stages: (1) theanalysis of the dose–effect response; (2) from its result,the calculation of the soil state parameter value; and (3),on its basis, the environmental assessment of the qual-ity of the soil (or another natural environment) using afive-point schedule. Conventional methods were usedat the first stage, and the results of this work were usedat the second and third stages.

DERIVATION OF A SOIL STATE PARAMETER

We proceed from the assumption that the soil statehas its own measure and is characterized by some quan-titative parameters

p

at each time moment. Each state ofthe soil corresponds to multiple and various responsesto the load, e.g., in the form of specific implementationsof its ecological functions or the soil biota state. If theresponse is measurable, it can be quantitatively

DEGRADATION, REHABILITATION, AND CONSERVATION OF SOILS

Methodology for the Environmental Assessment of the Soil State and Regulation of the Soil Quality

A. S. Yakovlev

a

, V. M. Gendugov

b

, G. P. Glazunov

a

, M. V. Evdokimova

a

, and E. A. Shulakova

a

a

Faculty of Soil Science, Moscow State University, Moscow, 119992 Russia

b

Faculty of Mechanics and Mathematics, Moscow State University, Moscow, 119992 RussiaE-mail: [email protected]

Received June 10, 2008

Abstract

—A method of determining the parameters of the soil state and assessing the quality of soils subjectto anthropogenic loads was substantiated. An equation for the state function was derived in the general form,and a method was proposed for determining the equation parameters based on the interpretation of the experi-mental “dose–response” relationships. A schedule was developed for the environmental assessment of the soilquality. Procedures were substantiated and specified for assessing the state and quality of erosion-hazardoussoils, the quality of the air with an unorganized dust source, the state and quality of the water on the basis ofbiotesting, the integrated assessment of the soil state under multiple loads, and the environmental–economicassessment of the quality of lands and waste disposal sites.

DOI:

10.1134/S1064229309080109

EURASIAN SOIL SCIENCE

Vol. 42

No. 8

2009

METHODOLOGY FOR THE ENVIRONMENTAL ASSESSMENT OF THE SOIL STATE 917

expressed in the form of the soil response

R

to a load.The problem is to find a substantiated uniform methodfor recording the load response parameter

R

on theschedule of the state parameter

p

.Assume that a soil has a response parameter

R

(e.g.,in the form of the relative number of soil organismsdead at known toxicant concentrations in the soil) anda quantitative state parameter

p

depending on theresponse

R

, and write their balance relationships. Forthis purpose, we separate an elementary soil unit in theform of a cube with the sides

x

,

y

,

and

z

. Thechange in the parameter

p

in a time unit in this soil vol-ume is equal to the sum of the parameter input throughthe cube faces and its generation within the volume. Itfollows from this definition that

where

U

,

V

,

and

W

are the input rate projections of thestate parameter and the response;

t

is the time; and

x

,

y

,

and

z

are the coordinates.Expanding (

Up

), (

Vp

), and (

Wp

) into a Taylor seriesand reducing by

x

,

y

,

z

,

and

t

, we obtain

(1)

where

f

(

p

)

is the generation rate of the state parameter.Supposing that

g

(

R

)

is the generation rate of theresponse parameter, we write the balance equation ofthe response parameter in a similar way:

(2)

Let us consider the simplest case, when the inputs of thestate and response parameters through the faces of theunit volume are negligible, which is implemented at

U

=

V

=

W

= 0; then, eqs. (1) and (2) take the form

(3)

(4)

The functions

f

(

p

)

and

g

(

R

)

are unknown. However,some ideas of their desirable properties can be takenfrom the necessary generalization of the experimentalresponses to the load according to the scheme “dose –effect” [2, 6]. In particular,

f

(0) =

g

(0)

= 0, and the stateparameter as a function of the response parameter

p

=

p

(

R

)

should be

S

-shaped and vary from 0 to 1.To find

p

, we expand the functions

f

(

p

)

and

g

(

R

)

in theneighborhood of

p

= 0,

g

= 0. Because

f

(0) =

g

(0)

= 0,

∂p∂t------∆x∆y∆z∆t

= Up x y z, ,( ) Up x ∆x+ y z, ,( )–[ ]∆z∆y∆t

+ Vp x y z, ,( ) Vp x y ∆y+ z, ,( )–[ ]∆x∆z∆t

+ Wp x y z, ,( ) Wp x y z ∆z+, ,( )–[ ]∆x∆y∆t

+ f p( )∆x∆y∆z∆t.

∂p∂t------ ∂pU

∂x----------- ∂pV

∂y---------- ∂pW

∂z-----------+ + + f p( ).=

∂R∂t------ ∂RU

∂x----------- ∂RV

∂y----------- ∂RW

∂z------------+ + + g R( ).=

dpdt------ f p( );=

dRdt------- g R( ).=

these expansions have the form f(p) = a1p + a2p2 + …;g(R) = b1R + b2R2 + …. We confine ourselves to the firstexpansion terms. Then, eqs. (3), and (4) take the form

Divide the former equation by the latter:

Taking the integral of this equation, we have

where c is the integration constant.We obtained the simplest form for the soil state

function. This function meets all the requirements,except for the S-like shape. Continuing the search, weconsider the use of the functions f(p) = a1p and g(R) =b1R + b2R2, i.e., the use of the second expansion terms.Taking into consideration that the requirement for theS-like shape should be met, we drop the linear term ofthe expansion g(R) and consider the equationg(R) = b2R2. Then, we obtain a set of eqs. (3) and (4) inthe form

We again divide the former equation by the latter:

Taking the integral of this equation, we have

or

(5)

where α = > 0, γ = const.

In the strict sense, the unknown values of γ and αshould be determined for each specific response to aspecific load. In practice, they are determined by com-posing and solving a set of two equations (5) for thepair of R values of the response function, one of whichwas derived for the maximum load that does not movethe system from the normal state (the critical load inecotoxicology [6] or the lower environmentally permis-sible level in the biotic approach to regulation [1, 2]),and the other was derived for the minimum load assur-edly moving the system beyond the limits of possiblerestoration in the considered time scale (the maximum

dpdt------ a1 p;

dRdt------- b1R.= =

dpdR-------

a1 pb1R---------.=

P cR

a1

b1-----

.=

dpdt------ a1 p;

dRdt------- b2R2.= =

dpp

------a1

b2-----dR

R2-------.=

plna1

b2----- 1

R---– γln+=

p γ e

αR---–⎝ ⎠

⎛ ⎞

.=

a1

b2-----

918

EURASIAN SOIL SCIENCE Vol. 42 No. 8 2009

YAKOVLEV et al.

toxic effect in ecotoxicology or the upper ecologicallypermissible level in the biotic approach).

The former R value (equal to the response to the crit-ical load) was related to an arbitrary value of the stateparameter. Its selection defined the division value of thesoil quality scale, which was based on the “norm–pathology” state parameter scale. In this case, it wastaken as p = 0.25, having in view the development of afive-point quality schedule.

The latter R value, which was equal to the responseto the load with the highest toxic effect, was related to themaximum possible value of the state parameter p = 1. Thisparameter value was also related to the responses R toall the higher loads. Thus, all the possible load valuesexceeding the load of the highest toxic effect corre-sponded to p = 1, and the load values that did not movethe soil from the background state were in the range of0 ≤ p < 0.25. The scale range of 0.25 ≤ p < 1 corre-sponded to all the transitional states.

As a result, we obtained the simplest form of the soilstate function, which also met the requirement for theS-like shape. For sufficiently high R values, the expo-nent in Eq. (5) tends to zero, and p γ. With consid-eration for the probabilistic nature of the state parame-ter, the state function was determined in the range of0 ≤ p ≤ 1; therefore, at p = 1, γ > 1 in all the cases.

INTEGRATED ASSESSMENT OF THE SOIL STATE UNDER MULTIPLE LOADS

We confine ourselves to the consideration of addi-tive loads in the first approximation. They cannot beconsidered inconsistent in statistical terms. At thesimultaneous action of several additive loads, the stateparameter for each load should be estimated from Eq.(5), and the individual estimates should be summarized.The soil state parameter is defined as a function of thesoil (biota) response to a load (e.g., the toxicant concen-tration). The load response function characterizes thedeath frequency of the test organisms. Therefore, theload response functional is the estimated probability ofdeath of the test organisms (or another similar event,e.g., the depletion of the soil profile due to erosion).The definition of the soil state function not only charac-terizes the probability of a specific event (the death of atest organism) but it also agrees with the conventionalrequirement for the estimate of a statistical characteris-tic: “the estimate of a statistical characteristic (proba-bility, distribution function, mathematical expectation,variance, correlation moment, etc.) is such a function ofthe experimental results that can be taken as a suitablemeasure of the estimated characteristic” [8].

Accepting the probabilistic nature of the separatestate parameters and considering them compatible, weshall calculate the generalized estimate by summingtheir probabilities [8].

Let us consider the case with the responses A and Bto two loads under whose effects the state estimates

were obtained in the forms of p(A) and p(B). The gen-eralized state estimate has the form

p(A + B) = p(A) + p(B) – p(A) p(B). (6)

In the case of three responses A, B, and C to threeloads, the state estimates under their effects wereobtained in the form of p(A), p(B), and p(C). It can beseen that, using the variable change method, the stateestimate under the three loads can be reduced to an esti-mate under two loads:

p(A + B + C) = p(D + C) = p(D) + p(C) – p(D)p(C),(7)

where A + B = D and p(A + B) = p(D). The substitutionof the p(D) value from Eq. (6) into Eq. (7) gives a gen-eralized estimate of the soil state under the three loads:

(8)

A united integrated estimate of the soil state (on a0–1 schedule) can thus be obtained for any number ofits responses to independent loads, whose validity islimited by the assumptions taken.

INTRODUCTION OF A SCHEDULE FOR THE ECOLOGICAL ASSESSMENT

OF SOIL QUALITY

The properties of the soil state parameter, which is afunctional of the load in the form of Eq. (5), allow it tobe used for the development of a schedule for regulat-ing the soil quality in the space of abiotic factors (loadparameters). Based on the continuity considerations[4], we selected a five-point schedule. The soils thatwere not moved by the load beyond the limits of thepossibility of self-restoration (p < 1) according to theprinciples of ecotoxicology [6] were classified into thefirst four quality categories (Table 1) in accordancewith the calculated p values, and the soils that weremoved beyond these limits (p = 1) were classified intothe fifth category. By definition, the state of the soils ofthe fifth category corresponds to a disturbed ecosystemwith its viability almost completely lost. The state ofthe soils of the first category corresponds to a back-ground ecosystem with high viability. The state of thesoils of the second, third, and fourth categories corre-sponds to ecosystems with unstable viability [2].Therefore, the width of the class interval on the stateschedule was found by division of the state-parametervariation range by four: 1/4 = 0.25. The limits of theclass intervals found using this value (0, 0.25, 0.5, 0.75, 1)on the state estimate schedule should be transferredonto the response schedule using Eq. (5) and then ontothe load schedule. That is, with the values of α and γbeing found from Eq. (5), the values of R should befound for p = 0.25, 0.5, 0.75, and 1 and substituted intothe response function to determine the argument values,which are the limits of the soil quality classes in thefield of the abiotic factor (load). The form of the load

p A B C+ +( ) p A( ) p B( ) p C( ) p A( ) p B( )–+ +=

– p A( ) p C( ) p B( ) p C( )– p A( ) p B( ) p C( ).+

EURASIAN SOIL SCIENCE Vol. 42 No. 8 2009

METHODOLOGY FOR THE ENVIRONMENTAL ASSESSMENT OF THE SOIL STATE 919

response function R should be preliminarily deter-mined.

Any two values of the response function R deter-mined experimentally or calculated from the model canbe used as the initial data for the determination of α and γ.Depending on the type of load, the determination of theload response function and its critical values can be aproblem of ecotoxicology [2, 6, 7], agrophysics [10],erosion science [3, 5], or other disciplines [1]. The fea-tures of the load in the region of its permissible valuesand the corresponding soil states (Table 1) should bespecified from the experimental data.

THEORY APPLICATION

Assessing the state and quality of eroded soils. Soilerosion is one of the few phenomena when the soil itselfcan serve as an indicator of the soil response to a loadaccording to the “dose–effect” rule. In this case, thedose is the fraction (layer) of the soil lost through ero-sion during a known time period, and the effect is thedegree of conservation of the original soil layer duringthe same time period due to the combined effect of ero-sion and pedogenesis. When the erosion rate is lowerthan the pedogenesis rate, the thickness of the soil pro-file increases or remains constant: this situation corre-sponds to the norm. When the erosion rate is higher, thethickness of the soil profile decreases up to its completedisappearance: this situation corresponds to pathology.

Let us consider the procedure for the assessment ofthe eroded soil quality using state functional (5). Weexpress the soil response to the eroding effect of windor a temporary water flow on a slope with the averagevelocity U as follows:

R = y(U) = qe/qi, (9)

where qe is the soil erosion rate expressed in kg/unit ofarea per year, and qi is the increment of the soil weightdue to the natural pedogenesis expressed in the sameunits. To determine the constants γ and α, a systems oftwo equations (5) for two R values should be composedand solved. In the limit norm state, the erosion rate isequal to the pedogenesis rate qe = qi; therefore, fromEq. (9), R = 1. The quantitative measures were selectedto be p = 0.25 for the limit norm state and p = 1 for thelimit pathology state. We supposed that the pathologystate arrived when the erosion rate exceeded the pedo-

genesis rate by 10 times (R = 10); then, it follows fromEq. (5) that

(10)

Solving system (10), we obtained γ = and

α = ln0.25 in Eq. (5) (Fig. 1); thus, we could quan-

titatively assess the state of soils and classify themaccording to the erosion rate (Table 2).

The obtained dimensionless limit values of theresponse R for five categories of soil quality in terms ofthe soil erosion (Table 2) were converted into thedimensionless limits of class intervals on the erosion-rate schedule. For this purpose, the conventional valuesof the average annual permissible soil loss [5] weretaken as the norms ensuring R = qe/qi = 1 (Table 3).Thus, we obtained the lower and upper limits of theclass intervals for five quality categories of eroded soilson the soil-loss rate schedule under the assumptionstaken (Table 4).

0.25 γ e α–=

1 γ eα10------–

=⎩⎪⎨⎪⎧ 0.25ln γln α–=

0 γln α10------–=

⎩⎪⎨⎪⎧

.

e19---– 0.25ln

109------–

Table 1. Soil quality categories based on the assessment of the soil state

p Category Load Soil state

0 ≤ p ≤ 0.25 1 below the toxic effect background homeostatic state with high viability

0.25 < p ≤ 0.5 2 toxic effect transitional

0.5 < p ≤ 0.75 3 toxic effect transitional

0.75 < p < 1 4 toxic effect transitional

p = 1 5 maximum toxic effect disturbed homeostasis state with strong irrevers-ible changes

p

1 3 4 5 6 8 9 10qe/qi

0

0.25

0.50

0.75

1.00

72

Fig. 1. The state parameter equation (5) for an eroded soil(the arrows denote the transfer of the class limits (Table 1)from the state schedule to the load response schedule).

920

EURASIAN SOIL SCIENCE Vol. 42 No. 8 2009

YAKOVLEV et al.

Now, to assess the quality of the eroded soil, the cal-culated (by simulation) or experimental (by observa-tion) soil loss rate (t/ha per year) should be determinedusing one of the known methods [5], and the qualitycategory of the eroded soil should be found on theschedule (Table 4) from the determined erosion rate.The permissible soil loss rate depends on the type ofsoil (Table 3). This solution is limited by the supposedpossibility of a ten-fold excess of the erosion rate overthe pedogenesis rate.

Assessment of the air quality over an unorganizeddust source. According to WHO data (1992), the dustraised and transported by the wind is an essential sourceof xenobiotics [6]. The unorganized sources of dustinclude land areas occupied by finely dispersed mate-rial capable of rising in the air under the effect of windor air flows created by industrial machines. Dust can belifted only by flows whose velocity is higher than a crit-ical (threshold) value for the soil or finely dispersedmaterial on its surface. Air dustiness is a phenomenonattending wind erosion; therefore, its quantitativeparameters can serve as an indicator of the dose–effectresponse, where the dose is the wind velocity and theeffect is the air dust content. Under normal conditions,when the wind velocity does not exceed the criticalvalue, no dust (from the unorganized sources) is presentin the air. An unlimited increase in the wind velocityresults in an unlimited increase of the solid phase con-centration in the air according to the out blowing law[3]. There is obviously a wind velocity at which thedust concentration in the air reaches the MPC level. We

classify this state as pathological. The indicator of theair dustiness as a function of the wind velocity is knownfrom the theory of soil erosion by wind [3]; therefore,we use Eq. (5) in the study of the air state above thewind-eroded surface and in the regulation of the airquality using a five-point schedule.

This is possible because the concentration of liftedsoil particles cw in the air layer adjacent to the erodedsurface is equal to the mass exchange parameter B (thesoil characteristic in the equation of soil erosion bywind [3]); thus, it follows that

(11)

where Bc is the critical value of the mass exchangeparameter, which has the dimension of the concentra-tion; α is the soil constant; Uc is the critical wind veloc-ity; and U is the actual wind velocity.

In the strict sense, Eq. (5) was derived by neglectingthe fluxes of the response and state parameters throughthe faces of the unit volume, which excludes its directapplication to moving air but does not exclude its appli-cability to an eroded soil. The extrapolation of theresults obtained for the soil to the surface air layer ispossible, because the mass exchange parameter by def-inition is equally related to the soil and the surface airlayer. Therefore, the conclusions from the theory devel-oped can be applied in the form of Eq. (5) to the surfaceair layer. The dust concentration in the air estimatedfrom the equation is an estimate of the possible maxi-mum value measured by a dust sensor installed at thestandard height (2 m). It approaches the true value withthe decreasing of the dust particles' size [3].

We take R = y(U) = as the response function of

the eroded soil to the impact of wind with the velocityU. When the dust concentration in the air reaches cMPC,the state is considered pathological. In this case, R = 1

cw Bc αsUc

2

U2------ 1–

⎝ ⎠⎜ ⎟⎛ ⎞

– .exp=

cw

cMPC----------

Table 2. Quality categories (points) of eroded soils based onthe assessment of the soil state (Fig. 1)

p R = qe/qi Category Load

0 ≤ p ≤ 0.25 0 ≤ R ≤ 1 1 does not decrease the profile depth

0.25 < p ≤ 0.5 1 < R ≤ 1.8 2 decreases the soil profile depth

0.5 < p ≤ 0.75 1.8 < R ≤ 3.5 3 "

0.75 < p < 1 3.5 < R < 10 4 "

p = 1 R ≥ 10 5 "

Table 3. Average annual maximum soil loss tolerance, t/ha ([5])

SoilsDegree of erosion

noneroded and slightly eroded medium-eroded strongly eroded

Soddy-podzolic and light gray forest soils on loess-like and other unconsolidated rocks

2.0 1.5 1.0

Gray and dark gray forest, chernozemic, and dark chestnut soils

2.0 2.0 1.5

Chestnut, light chestnut, and sierozemic soils 1.5 1.5 1.0

Soils developed on opokas and chalks 1.0 0.5 0.5

EURASIAN SOIL SCIENCE Vol. 42 No. 8 2009

METHODOLOGY FOR THE ENVIRONMENTAL ASSESSMENT OF THE SOIL STATE 921

and p = 1. The wind velocity corresponding to this statecalculated from Eq. (11) is as follows:

(12)

The properties of the soil as a dust source are con-sidered constant during the short out blowing period.

The norm state is realized at all wind velocities notexceeding the critical velocity. The norm limit isreached at a wind velocity equal to its critical value,when, according to Eq. (11), cw = Bc, the response func-

tion is B = y(U) = = , and the state parameter

is p = 0.25.

UUc

2

1cMPCln Bcln–

αs---------------------------------–

-------------------------------------------.=

cw

cMPC----------

Bc

cMPC----------

Comparing Eq. set (5) for the norm and the pathol-ogy, we have

(13)

Solving Eqs. (13), we obtain γ = and

α = ln0.25/ These values are necessary for

assessing the air state from Eq. (5) during the soil ero-sion by wind under the above limiting conditions.

The state equation (5) is suitable not only for thereal-time monitoring of the dust content in the air abovean unorganized source but also for its regulation using afive-point schedule (Table 1). This requires knowledgeof the MPC and all the soil constants (Bc, α, and Uc).Taking into consideration that the MPC values for thedust in the air were specified for a height of 2 m above thesoil surface and the mass exchange parameter character-izes the concentration of soil particles in the surface airlayer, the direct application of the theory without correc-tion for the height can result in an error. Correctionsshould be calculated using the reported data [3].

Biotesting estimation of the water state and quality.Let a biotest result in the response R = y, which repre-sents the fraction of dead test organisms as a functionof the contaminant concentration c in a solution (a soilsolution; a water extract from soil or waste disposalmaterial; and the water from a water body, municipalsupply, or another source):

y = f(c).

The plot of a typical response function of organismsto the contaminant concentration (Fig. 2) has a mini-mum, which is indicative of the fact that the contami-nant first stimulates the living organisms and theninhibits them. At c = 0 and c = c0, the fractions of deadorganisms y coincide and f0 = f(c0); at c = c∗, this frac-tion is equal to 1. According to Kutsenko [6], c0 is thethreshold dose, and c∗ is the maximum toxic dose.

In the biotesting estimation of the state of contami-nated water, we use Eq. (5) and suppose that the exper-imental conditions are within the range of its limita-tions. We find γ and α in Eq. (5) from the condition thatp = 0.25 at R = f(c) = f(c0) and p = 1 at R = f(c) = f(c∗).Then, Eq. set (5) has the form

(14)

0.25 γ e

αcMPC

Bc----------------–

=

1 γ e α–=⎩⎪⎨⎪⎧

0.25ln γlnαcMPC

BÍ---------------–=

0 γln α–=⎩⎪⎨⎪⎧

.

e0.25/ 1

cMPC

Bc-----------–⎝ ⎠

⎛ ⎞ln

1cMPC

Bc----------–⎝ ⎠

⎛ ⎞ .

0.25 γ e

αf c0( )-------------–

=

1 γ e

αf c*( )-------------–

=⎩⎪⎨⎪⎧ 0.25ln γln α

f c0( )-------------–=

0 γln αf c*( )-------------–=

⎩⎪⎪⎨⎪⎪⎧

.

Table 4. The lower (above the line) and upper (under theline) limits of class intervals of the soil-loss schedule for fivequality categories of eroded soils from Table 3 (the upperlimit is not included in the class interval)

CategorySoil loss tolerance, t/ha (Table 3)

0.5 1.0 1.5 2.0

1 0.0/0.5 0.0/1.0 0.0/1.5 0.0/2.0

2 0.5/0.9 1.0/1.8 1.5/2.7 2.0/3.6

3 0.9/1.75 1.8/3.5 2.7/5.25 3.6/7.0

4 1.75/5.0 3.5/10.0 5.25/15.0 7.0/20.0

5 5.0/abs 10.0/abs 15.0/abs 20.0/abs

c0 c*

y

c

Fig. 2. General shape of the fraction of dead test organisms asa function of the contaminant concentration in the solution.

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YAKOVLEV et al.

When the contamination response function R = f(c) isknown, the state function (5) has the final form

(15)

where γ = exp

α = ln0.25/ f(c0) is an experimental

constant equal to the fraction of the test organisms deadat the threshold dose (load), and c(c∗) is an experimen-tal constant equal to the fraction of the test organismsdead at the maximum toxic dose (load).

Different approaches to the selection of criteria forthe threshold doses are used depending on the aims andobjectives of the studies [2, 6, 11]. Using the solution ofTerekhova ([9], p. 160, Fig. 36), we assess the state ofwater containing zinc ions (mg/l) with considerationfor the experiment on the accumulation of Phomaglomerata mycelial biomass during the cultivation inthis water:

Taking into consideration that the inhibiting effect ofzinc was manifested when its concentration corre-sponded to the minimum on the plot (Fig. 2), we takec0 = 0.1, thus neglecting the details of the response thatare insignificant for our purposes within the concentra-tion range corresponding to the first category of quality.We took the inhibition parameter of the fungal myceliumgrowth under the effect of zinc equal to unity minus thedaily biomass increment divided by 0.191 (a value insig-nificantly differing from 0.19 was selected to avoid divi-sion by 0 in the calculation of p) as the contaminationresponse function R. The selected parameter (derivativefrom the daily increment) increased from an almost zerovalue (0.00523) at c0 = 0.1 mg/l to 1 at c = 300 mg/l. Wetook that the parameter was null at the zero concentra-tion. As a result, from Eq. (15), we have α = 0.007296and γ = 1.007323.

Setting the limits of the class intervals on the stateparameter schedule p, we calculated the missing R val-ues (for p = 0.5 and p = 0.75) from Eq. (15) using thefound values of α and γ then, the corresponding con-centrations of zinc in the water were determined fromthem by linear interpolation through two neighboringpoints. Thus, for the five categories of water quality deter-

Zn2+, mg/l 0 0.1 1 10 100 300

Daily incre-ment, mg/ml

0.18 0.19 0.17 0.07 0.02 0

Fungus inhibi-tion parameter, R

0 0.00523 0.01099 0.63351 0.89529 1

p γ eα

f c( )-----------–

,=

0.25/ 1f c*( )f c0( )-------------–⎝ ⎠

⎛ ⎞ln⎝ ⎠⎛ ⎞ ,

1f c*( )------------- 1

f c0( )-------------–⎝ ⎠

⎛ ⎞ ,

mined from the effect of zinc on Phoma glomerata, thefollowing zinc concentrations c (mg/ml) were obtained:

Integrated assessment of the water quality undermultiple loads. We assess the state of a soil contami-nated by two different substances with independentharmful effects. Let the state parameter as a functionalof the response to the harmful impact take the value

for the first contaminant and the value for thesecond contaminant and the responses not be corre-lated. Then, with consideration for the effects of bothcontaminants, the state parameter is found from Eq. (6):

Let = 0.5 and = 0.4; then, p = 0.5 + 0.4 –0.5 × 0.4 = 0.7, which corresponds to the third category(3 points) of the soil quality (Table 2).

Now, we assess the quality of a soil also contami-nated by a third independent contaminant, for whichthe response functional is = 0.8. Then, using theestimate for the former two contaminants equal to 0.7,we find the parameter of the soil state under the effectof the three contaminants from Eq. (6): p = 0.7 + 0.8 –0.7 × 0.8 = 0.94. This corresponds to the fourth cate-gory (4 points) of the soil quality (Table 2).

The presented example shows that, under severalloads and the corresponding individual state parame-ters, the resultant soil state parameter is higher than thehighest parameter of the components. Note that thisconclusion is drawn under the assumption of the addi-tivity and no inconsistency of the loads.

Restoration of the limits of the soil contaminationquality categories in the field of the contaminant con-centrations from the biotesting results. The procedurefor the restoration of the class limits in the field of thecontaminant concentrations for five categories of thecontaminated soil quality is reduced to the use of theabove scale of the ecological soil quality. For this pur-pose, the experimental determination of the loadresponse (the contaminant concentration in the soil)should be performed for at least two out of the five pos-sible load values corresponding to the class limits(0, 0.25, 0.5, 0.75, 1), which are qualitatively character-ized in Table 3. Using such results, Eq. set (5) can becomposed and solved to determine the coefficients γand α for a specific soil and a specific contaminant.Given these coefficients, the R values of the soilresponse to a load can be determined from Eq. (5) forthe other three p values. For the final solution of theproblem set (the restoration of the limits of the contam-inated soil quality categories in the contaminant con-centration field), the five found values of the response

category 1 2 3 4 5

range c 0 < c ≤ ≤ 0.1

0.1 < c ≤ ≤ 0.9

0.9 < c ≤ ≤ 1.2

1.2 < c < < 300

c ≥ ≥ 300

pM1pM2

p pM1pM2

pM1pM2

–+ .=

pM1pM2

pM3

EURASIAN SOIL SCIENCE Vol. 42 No. 8 2009

METHODOLOGY FOR THE ENVIRONMENTAL ASSESSMENT OF THE SOIL STATE 923

R should be plotted in the field of the contaminant con-centrations. For this purpose, the form of the responseequation B = f(c) should be specified. In the most gen-eral form, the response curve has the shape shown inFig. 2. The determination of this relationship is a typi-cal problem of ecotoxicology, and some reported equa-tions can be used [6, 11].

Based on the aforesaid and the available data for theinterpretation of the response to the load [2, 7], the pre-liminary (quite obvious) conclusion can be drawn thateach contaminant corresponds to a series of coefficientsγ and α, as well as to the quality category limits, in theconcentration field depending on not only the soil typebut also on other natural factors. It is clear that the qual-ity category limits recommended for the contaminatedsoils in the concentration field [4] can be used only inthe first approximation. To ensure an adequate assess-ment of the state of the contaminated soils, these limitsshould be determined using the proposed procedure onthe basis of works on biodiagnostics or biotesting.

The environmental–economic assessment of agri-cultural land quality. The economic assessment of agri-cultural lands is determined by the cost of the produc-tion, which depends on the yield and quality of thecrops. The cadastre assessment is finally determined bythe fertility of the soil, whose contamination should beautomatically taken into consideration through thedecrease in the crop yield. However, the cadastreassessment is performed by calculations and does notconsider the local contamination of the soil. We shallanalyze a method for considering the effect of contam-ination on the economic assessment of the soil, i.e., theenvironmental–economic assessment of agriculturallands.

Changes in the amount and quality of the crops,including those under the effect of soil contamination,should result in proportional changes of the economicestimate; the scaling coefficient can serve as a correc-tion factor for the cadastre assessment. This is a calcu-lated rather than an empirical coefficient; therefore, itsdevelopment faces problems related to (1) the simulta-neous determination of its quantitative (crop) and qual-itative (degree of contamination) parameters and (2) theconsideration of the economic environment. With thisin mind, we separate the two coefficient components—one of which depends on the economic environment,while the other is determined only by the natural pro-cesses—and restrict ourselves to the consideration ofthe latter component. We designate this component asthe correction factor q.

This coefficient should (1) not be arbitrary,(2) reflect the actual relationship between the contami-nation of the soil and its productivity, (3) reflect theactual relationship between the contamination of thesoil and that of the crops, and (4) be suitable for theconsideration of several contaminants and agriculturalcrops. The above data indicate that the soil state param-eter p based on the interpretation of the biotic response

to the load (including soil contamination) has all theabove properties and can be used as a correction factor.

It is noteworthy that not only the amount of the yieldbut also its quality (which obviously deteriorates withincreasing concentrations of contaminants in the crops)can be taken into consideration using this coefficient.Using the concentration of a contaminant (e.g., a heavymetal) in the crop as a dose–effect response, the soilstate parameter in terms of this contaminant can be cal-culated from experimental data and included into theintegrated estimate. This is of special importance,because the cadastre assessment of lands is based on theproductivity and does not consider the quality of theyield.

In technical terms, the value q = 1 – p should be usedas a correction factor, which simplifies the interpreta-tion of the results. For the background state of the soil,when p = 0, the correction factor q = 1. At the unaccept-able degree of soil contamination, when the crop yieldis zero and, hence, p =1 or the crop yield is not zero butits quality is so low that the soil state parameter in termsof the crop contamination p =1, the correction factorq = 0. Thus, the correction factor is a decreasing coeffi-cient, which decreases from 1 to 0 with increasing load.Let us consider the extreme cases. If the correction fac-tor q = 1, the cadastre value and, hence, the assessmentbasis turns into zero. In the interval between theextreme values, the correction factor varies nonlinearlydepending on the contaminant concentration accordingto Eq. (5), which finally reflects the effect of the bioticresponse to the contamination.

The proposed procedure for determining the correc-tion factor is applicable only for agricultural lands andonly for taxation purposes. This is related to the factthat this decreasing coefficient reflects the actualdecrease in the assessment basis (the cadastre value ofthe land) due to the decrease in the amount and qualityof the yield caused by the soil contamination, i.e., dueto reasons independent of the land user. If p = 1, the useof the correction factor gives a zero cadastre value ofthe land. This only signifies that the assessment basis iszero in this case.

On agricultural lands, the remediation ensures thequantitative and qualitative improvement of the yield,which is a reason for its implementation. This reason isabsent for nonagricultural lands. Therefore, the correc-tion factor should be an increasing coefficient deter-mined by the necessary costs of the damage compensa-tion and the soil restoration. Until their development,the coefficient in the form q = 1 + p (where p is the inte-grated state parameter) or a simpler parameter q = n(where n is the soil quality category, Table 1) can berecommended as a first approximation. The correctionfactor increases from 1 to 2 with the increasing soilcontamination in the former case and from 1 to 5 in thelatter case. In this approach, the cadastre value of theland and, hence, its assessment basis increase with the

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YAKOVLEV et al.

increasing soil contamination, which will stimulateremediation for decreasing this basis.

Note again that the proposed coefficients reflect theactual differences among the soils due to their environ-mental states.

Assessment of the state of waste disposal sites andthe regulation of their quality. A waste disposal site(WDS) is a three-dimensional body composed of amixture of domestic and municipal wastes interstrati-fied with soils and sediments and filled with specificgases releasing into the atmosphere and solutions infil-trating into the soil or directed to treatment facilities.According to normative documents, a WDS should notaffect the environment. However, the mass exchangebetween the WDS and the surrounding area cannot beexcluded for physical reasons related to the diffusion ofthe contaminants and the effect of water and air flows;therefore, contaminants do get into the environment.Therefore, the management of the legality requires themonitoring of the environmental components in thevicinity of WDSs.

Along with the technical and organizational prob-lems of the monitoring, essential methodological prob-lems are related to the diversity of the contaminationsources and the integrated assessment of the sites sub-ject to their effect. In technical terms, the monitoring isbased on the interpretation of the experimental dose–effect relationships most frequently determined bybiotesting the extracts; therefore, an integrated estimatecan be obtained using the method considered.

The assessment of the waste disposal material isperformed by calculations on the basis of the data forthe hazard classes and contents of components. In thiscase too, biotest results are the primary source of datafor determining the hazard class; therefore, the inte-grated assessment of the waste disposal material can beperformed using the state parameters and their integra-tion.

The hazard of the liquid component of the waste dis-posal material (filtrate) is also assessed using biotestingmethods, which opens up possibilities for the applica-tion of the procedure developed.

In addition, the use of this procedure allows a unitedintegrated estimate for the entire WDS to be performed.For this purpose, partial estimates of the WDS compo-nents should be combined using Eqs. (6)–(8).

RESULTS AND DISCUSSION

The procedure developed for assessing the state andregulating the quality of the soil under a load is a resultof development of the conventional approach based onthe analysis and interpretation of the dose–effect rela-tionship, where the dose is the quantitative measure ofthe load and the effect is the quantitative measure of thesoil functioning or the impact on living organisms. Theprocedure differs from the known ones by the methodof introducing a state parameter and determining its

analytical form. The state parameter is introduced as anindependent soil property that has a quantitative mea-sure and varies in a unit of soil volume in accordancewith the conservation laws due to in-situ generation andinput from the outside.

The state parameter imparted by the probabilisticproperties varies from 0 (which corresponds to thebackground state of the ecosystem) to 1 (which corre-sponds to its state disturbed to almost complete loss ofviability and irreversible changes on the time scale con-sidered).

According to this definition, the analytical form ofthe state parameter was found using the conventionalmechanics method of balance analysis. On its basis, thesoil state function was derived, which was a functionalof the soil response to the load. To determine the soilstate function parameters, either an equation of the soilresponse function to the load or experimental values ofthe response function to the known load values are nec-essary.

The state function equation was derived in the sim-plest form. A two-parametric S-shaped function vary-ing from 0 to 1 was obtained. Therefore, at least twofunction values for two argument values should beknown for determining the function equation parame-ters. The values of the load response function are argu-ments in this case. In the general case, these can be arbi-trary values, but, in practice, only two values of theresponse function are most frequently strictly deter-mined: those of threshold load and the maximum toxiceffect. Later on, if the accuracy of the state assessmentshould be improved for systems with a complex (e.g.,stepwise) load response function, the proposed methodallows the state function to be defined in another formwith the number of parameters higher than 2. This canbe attained by increasing the number of accounted forterms in the Taylor expansion. The larger the number ofparameters, the larger the number of values of the loadresponse function necessary for their identification;however, in any case, the form of the response functionequation and the derived state estimate are not arbitrary.

The nonarbitrary and probabilistic nature of thestate function ensured the comparability of the stateestimates under different loads and the possibility ofthe simultaneous consideration of the responses to dif-ferent loads: the possibility of an integrated stateassessment. It was shown that an integrated state esti-mate can be obtained by summarizing the individualestimates in accordance with the probability theory. Itwas found that the integrated state estimate is higherthan any individual estimate. This conclusion is limitedby the supposed additivity of the individual loadresponses and the absence of their inconsistency.

Based on the properties of the state function, a uni-form soil quality schedule with any number of qualityclasses or categories can be introduced. A five-pointquality schedule of the soil under a load was selectedfrom the continuity and usability considerations. Five

EURASIAN SOIL SCIENCE Vol. 42 No. 8 2009

METHODOLOGY FOR THE ENVIRONMENTAL ASSESSMENT OF THE SOIL STATE 925

points were given to the soils altered to an unacceptablestate (established by objective biotesting and biodiagnos-tic methods), including the irreversibility to the initialstate on the time scale considered, i.e., having the stateparameter equal to 1. One point was given to the soilsthat were at least not deteriorated and, in some cases,even improved or unchanged under the load compared tothe background soils. The selection of the five-pointschedule defined the division value of the quality scale(0.25) and the limit values of the quality categories divis-ible by this value. Thus, the unchanged soils and thosesubjected to reversible changes were classified into thefirst four quality categories in accordance with the stateparameter values; the soils subjected to unacceptablechanges, including the irreversible ones, and having thestate parameter equal to 1 were classified into the fifthquality category. The parameterization of the soil statefunction was based on the use of experimental or theoret-ical values of the response function. It was shown that thesoil response function can be derived not only using thedirect observations of biota or soil responses to the loadbut also by biodiagnostic methods in water extracts fromthe soil. The direct applicability of the developed proce-dure to the state assessment of water bodies with conven-tionally still water (ponds, lakes, and reservoirs) was thusexperimentally proved. It was also shown that, using theproperties of the out blowing equation, this procedurecan be directly applied to the state assessment and theregulation of dusty atmospheric air. Thus, along with thestate assessment of separate environmental components,the integrated assessment of the environment can be per-formed using the same quality schedule.

CONCLUSIONS

The soil state parameter based on the quantitativeanalysis of the response of the soil biota, the soil, or testorganisms to the applied load was substantiated. Thenew parameter is based on the summarizing of theknown methods for assessing the dose–effect responseto a load. The method of deriving the state function isnonarbitrary (objective) and allows improving the func-tion form with complicating the response to the load.The method considers a set of loads under the supposi-

tion of their additivity and the absence of inconsistency.It was shown that the state parameter can be expandedto other natural environments adjacent to the soil andinvolved in the mass exchange with it.

ACKNOWLEDGMENTSThis work was supported in part by the Russian

Foundation for Basic Research, project nos. 08-08-13724 and 08-01-12046.

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