**Prediction of Herbicides Concentration in Streams**

Raj Mohan Singh *Department of Civil Engineering, MNNIT Allahabad, India* 

#### **1. Introduction**

226 Herbicides – Properties, Synthesis and Control of Weeds

Walters, S.M. (1990). Clean-up techniques for pesticides in fatty foods. *Analytica Chimica* 

Natural and anthropogenic variables of stream drainage basins such as hydrogeologic parameters (permeability, porosity etc.), amount of agricultural chemicals applied, or percentage of land planted affect agricultural chemical concentration and mass transport in streams. The use of herbicides, pesticides, and other chemicals in agricultural fields increase the concentration of chemicals in streams which severely affects the health of human and environment. The transport of chemical pollutants into river or streams is not straight forward but complex function of applied chemicals and land use patterns in a given river or stream basin. The factors responsible for transport of chemicals may be considered as inputs and chemical concentration measurements in streams as outputs. Each of these inputs and outputs may contain measurement errors. Present work exploited characteristics of fuzzy sets to address uncertainties in inputs by incorporating overlapping membership functions for each of inputs even for limited data availability situations. Soft computing methods such as the fuzzy rule based and ANN (Artificial Neural Networks) is used for characterization of herbicides concentration in streams. The fuzzy c-means (FCM) algorithm is used for the optimization of membership functions of fuzzy rule based models for the estimation of diffuse pollution concentration in streams. The general methodology based on fuzzy, ANN and FCM for estimation of diffuse pollution in streams is presented. The application of the proposed methodology is illustrated with real data to estimate the diffuse pollution concentration in a stream system due to application of a typical herbicide, atrazine, in corn fields with limited data availability. Solution results establish that developed fuzzy rule base model with FCM outperform fuzzy or ANN and capable for the estimation of diffuse pollution concentration values in water matrices with sparse data situations.

Application of pesticides, insecticides and herbicides, cause diffuse pollution, commonly referred to as non-point source pollution in river or streams. Diffuse pollution from agricultural activities is a major cause of concern for the health of human and environment. Diffuse (non-dot, dispersed) pollution generally arises from land-use activities (urban and rural) that are dispersed across a catchment or subcatchment, where as point sources of pollution arise as a process industrial effluent, municipal sewage effluent, deep mine or farm effluent discharge (Novotny 2003, based on CIWEM (D'Arcy et al., 2000)). Potential point sources of pollution is characterised by its location, magnitude and duration of activity; and the sources of pollution is characterized when these parameters are identified

Prediction of Herbicides Concentration in Streams 229

physical, chemical, and biological processes as well as in the data acquisition and parameters for model algorithms. Consequently, the complexities of these models and their resource-intensive nature are significant obstacles to their application (Charbeneau and

There is a need for the development of simpler methods of agricultural stream quality predictions that provide the required information to the analyst and water managers with minimal effort and limited data requirements as compared to complex process models. As an alternative or supplement to complex runoff quality simulation models, fuzzy rule based model with FCM is proposed to estimate pollutant concentration due to applications of

The herbicide atrazine (2-chloro-4-[ethylamino]-6-[isopropylamino]-1,3,5-triazine), a chlorinated herbicide, has been one of the most heavily used herbicides in the world. Atrazine is toxic to many living organism. The maximum contaminant level (MCL) of atrazine is restricted to 3 µg/l for drinking water (USEPA, 2001). Because atrazine is water soluble, it has the potential to leach into ground water and run off to surface water. Atrazine is associated with developmental effects (USEPA, 2002), such as birth defects, structural anomalies, and adverse hormone changes. Thus, its accurate estimation in water matrices is

In this study, a fuzzy rule based model optimized by fuzzy c-Means, is developed to obtain the estimate of atrazine concentrations from agricultural run-off using limited available information. The work discusses the methodology to develop the fuzzy rule base model using annual average use of herbicide atrazine per unit area, extent of herbicide atrazine applied area and herbicide atrazine application season as inputs to fuzzy rule based model and observed herbicide concentration at the basin outlet as the output for the fuzzy model. The data of White River Basin, a part of the Mississippi River system, USA, is used for

Natural and anthropogenic variables of stream drainage basins such as hydrogeologic parameters (permeability, porosity etc.), amount of agricultural chemicals applied, or percentage of land planted affect agricultural chemical concentration and mass transport in streams. The general form of model that simulates the concentration measurement in a

where C is the stream agricultural diffuse pollution observed concentration measurement values; **W** is a vector of watershed characteristics; and **H** is a vector of hydrological variables such as precipitation, runoff, etc., and **A** is a vector of relevant agricultural practices

For a particular watershed, watershed characteristic, **W**, may be assumed to be constant. Also, for a particular hydrological unit, **H** may be assumed to be of similar characteristics.

C = f (**W, H, A**) (1)

C = f (**A**) (2)

**2. Agricultural diffuse pollution concentration simulation in streams** 

watershed can be represented by (Tesfamichael et al., 2005)

including actual chemical application rate in the field in lb/acre.

Then, Equation (1), though simplified, may be represented by

agricultural chemical, herbicide, atrazine, in the streams.

Barrett 1998).

imperative.

developing the fuzzy rule base model.

(Mahar and Dattta 2000; Singh and Datta, 2004, and Singh and Datta, 2006a and 2006b). In diffuse sources of pollution or non-point sources of pollution, sources of pollution is moving with polluting media thus making it more difficult and complex problem to solve.

Often diffuse pollution is individually minor but collectively constitutes significant sources at basin scale. Although nonpoint or diffuse sources may contribute many of the same kinds of pollutants, these pollutants are generated in different volumes, combinations, and concentrations (Jha et al., 2005). Thus, diffuse pollution comprises true non-point source pollution together with inputs from a multiplicity of minor point sources. The important characteristics of diffuse pollution are, therefore, not whether anyone can identify the source or sources, but the collective impact of diffuse pollutants and the mechanisms through which they move through the environment. The concept of diffuse pollution is useful because it explains features of pollution in receiving water bodies that differ from the point sources of pollution that are typically well characterized, monitored, and quantified. Some of the characteristics of diffuse pollutants are that the concentrations of some pollutants actually may increase with flow rather than it has diluted, pollution peaks are variable and difficult to predict, and impacts are often slow to develop and become evident years later (e.g. contamination of groundwater). For diffuse pollution, it is the proportion of the land use from which the pollution is derived, is more important.

Agricultural activities such as application of herbicides result in the contamination of surface water with agricultural chemicals. Numerous recent investigations (Goolsby and Battaglin, 1993 and 1995; Schottler et al., 1994; Baker and Richards, 1990) indicate that significant quantities of some herbicides are flushed from cropland to streams each spring and summer during rainfall events following the applications. Peak concentration of several herbicides can exceed 10 µg/l during these events (Coupe et al., 1995; Scribner et al., 1994). Pareira (1990), Crawfard (1995, 2001), Capel and Larson (2001), and Smith and Wheater (2004) in their studies on pesticides/herbicides, identified the major factors that control the pollutant transport. Herbicides and pesticides concentrations in surface waters are affected by natural and human factors. For example, concentrations of atrazine, a herbicide widely used on corn fields, tended to be higher in an agricultural basin with permeable, well drained soils, than in an agricultural basin with less permeable, more poorly drained soils (Crawfard, 1995). Capel et al. (2001) estimated the annual pollutant transport as percent of use (load as percent of use - LAPU). Larson and Gilliom (2001) developed a regression model for the estimation of pollutants.

Water resources professionals, managers and government authorities involved in surface water management are increasingly pressed to make appropriate decisions on land use and development policies such that these decisions will not adversely affect the health and environment. At the same time, they are constrained by inadequate budgets, limited resources, and incomplete information, which compel them to rely on models to evaluate or to estimate the pollution characteristics in the water bodies, and the implications of their decisions based on those evaluations. In this regard, the role of complex stream quality simulation models e.g. SWAT (Arnold et al. 1983), AGNPS (Young et al., 1989) etc. in evaluating runoff pollution conditions under various agricultural chemicals and land use patterns is also limited. These models incorporate rainfall, catchments, and pollutant characteristics, requiring extensive calibration and verification. However, their results are not without large uncertainties. These uncertainties arise both in the representation of the

(Mahar and Dattta 2000; Singh and Datta, 2004, and Singh and Datta, 2006a and 2006b). In diffuse sources of pollution or non-point sources of pollution, sources of pollution is moving

Often diffuse pollution is individually minor but collectively constitutes significant sources at basin scale. Although nonpoint or diffuse sources may contribute many of the same kinds of pollutants, these pollutants are generated in different volumes, combinations, and concentrations (Jha et al., 2005). Thus, diffuse pollution comprises true non-point source pollution together with inputs from a multiplicity of minor point sources. The important characteristics of diffuse pollution are, therefore, not whether anyone can identify the source or sources, but the collective impact of diffuse pollutants and the mechanisms through which they move through the environment. The concept of diffuse pollution is useful because it explains features of pollution in receiving water bodies that differ from the point sources of pollution that are typically well characterized, monitored, and quantified. Some of the characteristics of diffuse pollutants are that the concentrations of some pollutants actually may increase with flow rather than it has diluted, pollution peaks are variable and difficult to predict, and impacts are often slow to develop and become evident years later (e.g. contamination of groundwater). For diffuse pollution, it is the proportion of the land

Agricultural activities such as application of herbicides result in the contamination of surface water with agricultural chemicals. Numerous recent investigations (Goolsby and Battaglin, 1993 and 1995; Schottler et al., 1994; Baker and Richards, 1990) indicate that significant quantities of some herbicides are flushed from cropland to streams each spring and summer during rainfall events following the applications. Peak concentration of several herbicides can exceed 10 µg/l during these events (Coupe et al., 1995; Scribner et al., 1994). Pareira (1990), Crawfard (1995, 2001), Capel and Larson (2001), and Smith and Wheater (2004) in their studies on pesticides/herbicides, identified the major factors that control the pollutant transport. Herbicides and pesticides concentrations in surface waters are affected by natural and human factors. For example, concentrations of atrazine, a herbicide widely used on corn fields, tended to be higher in an agricultural basin with permeable, well drained soils, than in an agricultural basin with less permeable, more poorly drained soils (Crawfard, 1995). Capel et al. (2001) estimated the annual pollutant transport as percent of use (load as percent of use - LAPU). Larson and Gilliom (2001) developed a regression

Water resources professionals, managers and government authorities involved in surface water management are increasingly pressed to make appropriate decisions on land use and development policies such that these decisions will not adversely affect the health and environment. At the same time, they are constrained by inadequate budgets, limited resources, and incomplete information, which compel them to rely on models to evaluate or to estimate the pollution characteristics in the water bodies, and the implications of their decisions based on those evaluations. In this regard, the role of complex stream quality simulation models e.g. SWAT (Arnold et al. 1983), AGNPS (Young et al., 1989) etc. in evaluating runoff pollution conditions under various agricultural chemicals and land use patterns is also limited. These models incorporate rainfall, catchments, and pollutant characteristics, requiring extensive calibration and verification. However, their results are not without large uncertainties. These uncertainties arise both in the representation of the

with polluting media thus making it more difficult and complex problem to solve.

use from which the pollution is derived, is more important.

model for the estimation of pollutants.

physical, chemical, and biological processes as well as in the data acquisition and parameters for model algorithms. Consequently, the complexities of these models and their resource-intensive nature are significant obstacles to their application (Charbeneau and Barrett 1998).

There is a need for the development of simpler methods of agricultural stream quality predictions that provide the required information to the analyst and water managers with minimal effort and limited data requirements as compared to complex process models. As an alternative or supplement to complex runoff quality simulation models, fuzzy rule based model with FCM is proposed to estimate pollutant concentration due to applications of agricultural chemical, herbicide, atrazine, in the streams.

The herbicide atrazine (2-chloro-4-[ethylamino]-6-[isopropylamino]-1,3,5-triazine), a chlorinated herbicide, has been one of the most heavily used herbicides in the world. Atrazine is toxic to many living organism. The maximum contaminant level (MCL) of atrazine is restricted to 3 µg/l for drinking water (USEPA, 2001). Because atrazine is water soluble, it has the potential to leach into ground water and run off to surface water. Atrazine is associated with developmental effects (USEPA, 2002), such as birth defects, structural anomalies, and adverse hormone changes. Thus, its accurate estimation in water matrices is imperative.

In this study, a fuzzy rule based model optimized by fuzzy c-Means, is developed to obtain the estimate of atrazine concentrations from agricultural run-off using limited available information. The work discusses the methodology to develop the fuzzy rule base model using annual average use of herbicide atrazine per unit area, extent of herbicide atrazine applied area and herbicide atrazine application season as inputs to fuzzy rule based model and observed herbicide concentration at the basin outlet as the output for the fuzzy model. The data of White River Basin, a part of the Mississippi River system, USA, is used for developing the fuzzy rule base model.

### **2. Agricultural diffuse pollution concentration simulation in streams**

Natural and anthropogenic variables of stream drainage basins such as hydrogeologic parameters (permeability, porosity etc.), amount of agricultural chemicals applied, or percentage of land planted affect agricultural chemical concentration and mass transport in streams. The general form of model that simulates the concentration measurement in a watershed can be represented by (Tesfamichael et al., 2005)

$$\mathbf{C} = \mathbf{f} \text{ (W, H, A)}\tag{1}$$

where C is the stream agricultural diffuse pollution observed concentration measurement values; **W** is a vector of watershed characteristics; and **H** is a vector of hydrological variables such as precipitation, runoff, etc., and **A** is a vector of relevant agricultural practices including actual chemical application rate in the field in lb/acre.

For a particular watershed, watershed characteristic, **W**, may be assumed to be constant. Also, for a particular hydrological unit, **H** may be assumed to be of similar characteristics. Then, Equation (1), though simplified, may be represented by

$$\mathbf{C} = \mathbf{f} \begin{pmatrix} \mathbf{A} \end{pmatrix} \tag{2}$$

Prediction of Herbicides Concentration in Streams 231

statements and, therefore, the fuzzy logic is more in line with human perception (Zadeh, 2000). Fuzzy logic has an advantage over many statistical methods in that the performance of a fuzzy expert system is not dependent on the volume of historical data available. Since these expert systems produce a result based on logical linguistic rules, extreme data points in a small data set do not unduly influence these models. Because of these characteristics, fuzzy logic may be a more suitable method for diffuse pollution forecasting than the usual regression modeling techniques used by many researchers (e,g. Goolsby and Battaglin (1993); Larson and Gilliom (2001); and Tesfamichael et al. (2005) etc.) for estimation of

The most common way to represent human knowledge is to form it into natural language

The form in expression (4) is commonly referred to as the IF-THEN rule based form (Ross, 1997). It typically expresses an inference such that if a fact (premise, hypothesis, antecedent) is known, then another fact called a conclusion (consequent) can be inferred or derived. Fuzzy logic systems are rule base systems that implements a nonlinear mapping (Dadone and VanLandingham, 2000) between stresses (represented by consequents) and state variables (represented by antecedents). Creating a fuzzy rule based system may be summarized in four basic steps (Ross 1997; Mahabir et al. 2003; Singh and Singh 2005):

a. For each variable, whether an input variable or a result variable, a set of membership functions must be defined. A membership function defines the degree to which the value of a variable belongs to the group and is usually a linguistic term, such as high or

b. Statements, or rules, are defined that relate the membership functions of each variable

c. The rules are mathematically evaluated and the results are combined. Each rule is evaluated through a process called implication, and the results of all of the rules are

d. The resulting function is evaluated as a crisp number through a process called

Subjective decisions are frequently required in fuzzy logic modeling, particularly in defining the membership functions for variables. In cases such as in this study, where large data sets are not available to define every potential occurrence scenario for the fuzzification of model,

Membership functions used to describe linguistic knowledge are the enormously subjective and context dependent part of fuzzy logic modeling (Vadiee, 1993). Each variable must have membership functions, usually represented by linguistic terms, defined for the entire range of possible values. The key idea in fuzzy logic, in fact, is the allowance of partial belongings of any object to different subsets of universal set instead of belonging to a single set

to the result, normally through a series of IF–THEN statements.

combined in a process called aggregation.

expert opinion is used to create logic in the rule base system.

IF premise (antecedent), THEN conclusions (consequent) (4)

diffuse pollution concentration in streams or other water bodies.

**3.2.1 Fuzzy rule based system architecture** 

expression of the type,

low.

defuzzification.

**3.2.2 Membership functions** 

The **A** may be further represented by

$$\mathbf{A} = \mathbf{f} \left( \mathbf{A}\_{\mathbf{C},} \mathbf{A}\_{\mathbf{L}} \right) \tag{3}$$

where **AC** represent the vector of applied agricultural chemical characteristics such as type of agricultural chemical (insecticide, herbicides etc.), application rate, application season etc., and **AL** is the land use patterns such as type of crop grown, percentage of cropped area, etc.

Here, agricultural chemical considered is herbicide, atrazine, and crop considered is corn. In this study fuzzy rule based model with FCM simulates the stream system behavior from inputs of agricultural practices and corresponding observed concentration measurement values. In fact the model tries to emulate the mechanism that produced the data set. In this way, the mathematical description of the physical system is learned by the model, and therefore utilized as a tool for stream system simulation. The cluster centers of inputs and outputs obtained using FCM model, in essence, represents a typical characteristics of the system behaviour, and hence utilized in the formation of rule base of the fuzzy model.
