Proposal Antibiotics 1206

8/2/2019 Proposal Antibiotics 1206

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M.S. THESIS PROPOSAL

OFARIN KKDOAN

MODELLING ANTIBIOTIC TRANSPORT

AND MAPPING THE ENVIRONMENTAL

RISK IN THE MARMARA REGION BY

USING GEOGRAPHICAL INFORMATION

SYSTEMS (GIS)

Boazii University

Bebek-stanbul


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1. INTRODUCTION

As human population increases, livestock farming has become more intensive for some

decades (Venglovsky et al., 2009). Veterinary antibiotics (VAs), one type of the drugs

approved for agriculture, are among the most widely preferred for animal health and

management (Sarmah et al., 2006). A considerable quantity of VAs originates from increased

number of large-scale animal feeding operations for swine, poultry, and cattle (Zhao et al.,

2010). Antibiotics regarded as micropollutants affect both water quality and human health by

transportation to surface waters via runoff. Thus, there has been an increased concern about

the adverse effects of released antibiotics causing chemical pollution in the environment.

The primary objective of this proposed study is to investigate the transport of tetracycline,

sulphonamide andfluoroquinolone antibiotic groups, which were analyzed in the soil samples

collected from the Marmara region by using Geographic Information Systems (GIS) and

modelling techniques. Moreover, the environmental risk of agricultural antibiotic runoff in the

Marmara region will be mapped.

2. LITERATURE REVIEW

Antibiotics have been used for treating infectious diseases in animals since 1900s. In addition

to therapeutic purposes, they are used for promotion of food animal growth, control and

prevention of diseases. The antibiotic dosages vary from 3 to 220 g/Mg of feed according to

animal type, size and growth stage. As can be seen from Table 1., animals utilize certain

proportion of these dosages and rest of them enter the terrestrial environment as urine, faeces

or manure excreted by them because they are poorly absorbed in the animal gut (Kumar et al.,

2005).

Table 1. Pro ortion of Antibiotics Fed Excreted in Urine and Feces Kumar et al. 2005


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The treatment of animals on pasture is direct release of veterinary antibiotics to environment.

The most important indirect reason for contamination of water bodies such as rivers and lakes

is surface runoff of manure applied to lands as soil fertilizer since many of antibiotics cannot

be degraded in the manure (Lertpaitoonpan et al., 2009). Furthermore, manure is stored in the

tanks systems for a period of time before dispersing on fields which may cause leaching

through soil (Kim et al., 2010). Fresh manure production varies according to animal type and

data on livestock manure production can be presented as Table-2.

Table 2. Fresh manure production per 1000 lb live animal mass per day

(ASAE Standards, 2003)

Animal Type

Parameter Units *

Da

iry

Be

ef

Ve

al

Sw

ine

Sh

eep

Goat

Ho

rse

Layer

Br

oiler

Tu

rkey

Total Manure lb Mean 86 58 62 84 40 41 51 64 85 47

Differences within species according to usage exist, but sufficient fresh manure data to list these differences was

not found. Typical live animal masses for which manure values represent are: dairy, 1400 lb; beef, 800 lb; veal, 200

lb; swine, 135 lb; sheep, 60 lb; goat, 140 lb; horse, 1000 lb; layer, 4 lb; broiler, 2 lb; turkey, 15 lb; and duck, 3 lb.

* All values wet basis

Feces and urine as voided.

Parameter means within each animal species are comprised of varying populations of data. Maximum numbers of

data points for each species are: dairy, 85; beef, 50; veal, 5; swine, 58; sheep, 39; goat, 3; horse, 31; layer, 74;

broiler, 14; turkey, 18; and duck, 6.

If application of manure to agricultural lands exceeds recommended values, antibiotics bring

about significant environmental problems such as toxicity to soil flora and fauna also

antibiotic resistance in aquatic and terrestrial environment (Sarmah et al., 2006). Liu et al.

(2009) carried out seed germination test on filter paper and plant growth test in soil, soil

respiration and phosphatase activity tests to evaluate phytotoxic efects of different types of

antibiotics on plant growth and soil quality. The authors realized that these effects change

depending on the antibiotic type and plant sensitivity. Venglovsky et al. (2009) observed that

antibiotics which are strongly bound to soil and have low half-life value can remain in the soil

longer than others. Thus, they have a chance to be degraded easily so contamination of

surface water can be prevented. However, they insisted on that there was a concern for plants

which could take up them. Another important drawback of antibiotics is occurrence of

antibiotic resistance bacteria in terrestrial and aquatic media leading to untreatable human and

animal diseases due to subsequent antibiotic ineffectiveness (Kim et al., 2010). Jorgensen and

Halling-Sorensen (2000) have suggested that antibiotic resistant bacteria originate from

excessive production and consumption of antibiotics and low concentrations are responsible


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for this problem. More harmful bacteria come into existence so gene pool of microorganisms

changes. Such kind of bacteria are irreversible also cannot be eliminated (Kumar et al., 2005).

In order to protect the environment, the marketing of veterinary medicinal products is actively

regulated in the European Union (EU) by Directive 2004/28/EC (Montforts, 2006). Moreover

the antibiotics used for growth-promoting purposes were banned in Europe in 2006 (Kemper,

2008).

Several factors can affect the transportation of VAs in the environment. In addition to

antibiotic and soil properties; weather and surface water flow conditions affect transport

behaviour in terrestrial environments (Kumar et al., 2005). Kay et al. (2005) carried out some

pilot studies to evaluate the transport of veterinary antibiotics in overland flow following the

application of pig slurry to arable land by irrigating soil pilots. Some antibiotic types are

detected in runoff samples greater than the others because they have lower organic carbon-

water partitioning coefficient value.

Davis et al. (2006) conducted some rainfall simulation experiments for various types of

antibiotics. After spraying soil surface with a solution containing antibiotics, runoff samples

are collected and analyzed for aqueous and sediment antibiotic concentrations. They realized

significant differences in two phases of antibiotic concentrations due to different pseudo-

partitioning coefficients (P-PC; ratio of sediment concentration to runoff concentration) of

them. They stated that erosion control practices could be used to decrease agricultural runoff

of antibiotics with high P-PC. Similarly, Kim et al. (2010) carried out rainfall simulated

studies to evaluate the impact of different physicochemical properties of antibiotics on

transport of them. They found that sorption and persistence characteristics of various

antibiotics play role on runoff behaviour of aqueous and sediment phases of them.

Boxall et al. (2002) investigated the sorption behaviour of sulphonamide,

sulfachloropyridazine by performing field and laboratory studies to assess the risk of surface

water contamination. They found that sulfachloropyridazine is highly mobile in clay sites

thereby it would easily be transported to surface waters due to its low sorption potential. In

addition to sulphonamides, Blackwell et al. (2007) investigated surface run-off of

tetracyclines and macrolides antibiotic groups originating from pig slurry in sandy loam soil

under field conditions. They realize that manure management practices, the nature of the landand climate conditions play role in mass loss in runoff.


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Fate and transport processes of pollutants are important to evaluate environmental effects on

the subsurface and surface waters since they facilitate the understanding of mobility and

degradation processes of pollutants to control environmental pollution and develop water

management practices (Joyce et al., 2010). Thanks to modelling, contamination pathways and

sources of pollutant contamination in the landscape are identified thereby pollutant

concentrations are estimated at any point to assess the pollutant mitigation strategies to protect

water resources from contamination (Blenkinsop et al., 2008).

There are many models different from one another in terms of representing hydrological

processes and objectives. They can simulate surface runoff pollution or leaching of pollutants

through subsurface (Branger et al., 2009). Basically, models can be categorized into two

classes: analytical and numerical models. In analytical modelling, there is few input data since

more assumptions are done to simply the initial and boundary conditions, flow conditions,

porous media, as well as physical and (bio)chemical processes of the simulated pollutants.

Therefore analytical models are easy to use and compute. On the other hand, numerical

models can overcome more complex contaminant transport issues (Chu and Marino, 2007).

However, they require more input data and limited availability of input data sometimes hinder

mathematical models (Schriever and Liess, 2007).

The models studied in the literature for evaluating pesticide transport in surface runoff are

plentiful. On the other hand, modeling studies for antibiotics are scarce. Kay et al. (2005) state

the fact that a number of models such as PRZM, PELMO and GLEAMS recommended by

FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) could be used

for veterinary medicines since their physicochemical properties is similar to that of pesticides.

Huber et al. (1998) developed a transport model for pesticide runoff from agricultural areas to

surface waters in Germany. They used various spatial data related to climate, soil, and land

use in addition to pesticide application rates to estimate runoff losses of pesticides from fields.

As a result, they constituted runoff-susceptibility maps to determine the risk of runoff-losses

of pesticides. However, inadequate reliable information regarding pesticide transport

behaviour under site specific conditions caused limitation in the study.

Branger et al. (2009) developed a transport model namely PESTDRAIN to simulate pesticide

transport in a subsurface tile-drained field. This model as a promising tool for agricultural


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3. METHODOLOGY

An antibiotic transport model will be developed for the Marmara Region by using

Geographical Information Systems (GIS). GIS based maps and data system including land

use, land cover and antibiotic concentration obtained from previously analyzed in 30 soil and

8 manure samples will be used for creation of a conceptual model. In order to set up equations

used in hydrologic (rainfall-runoff) and hydrodynamic (pollutant transport) models, the

transport processes taking place in the model will be identified. ModelBuilder function

embedded in ArcGIS will be worked to turn conceptual model into GIS-based simulation

transport model. Thanks to establishment of spatially explicit calculation based on antibiotic

use, precipitation, topography, land use and cover, the environmental risk of antibiotics in the

Marmara region will be mapped.

3.1. Site Description

Marmara region is located in the northwest part of Turkey having approximately an area of

67,000 square kilometres and a total of more than 23 million people because of relatively high

immigration (Doan et al., 2007). In the region; industry, commerce, tourism and agriculture

have developed. Among the seven geographical regions, the region has lowest elevation.

Whereas the planted area accounts for 30 % of the region, the forests cover around 11.5 % of

the entire region. Forests are found in particularly Trakya region at high elevations. Wheat

forms more or less half of the cultivated areas and rest of these areas consists of mainly sugar

beets, corn and sunflower. Poultry raising and silk culture are widespread. Throughout the

region, there is a dense stream network despite its small scale. The main rivers are Sakarya,

Ergene, Susurluk, Meri and Biga. There are also many large and small natural and artificial

lakes such as Bykekmece, Kkekmece, Durusu, znik, Sapanca, Uluabat and Manyas.

The effects of black sea, terrestrial and mediterranean climates prevail in the region. The

annual precipitation is between 500 and 1000 mm. Since terrestrial climate increases towards

upcountry, the cold effect takes place rather than coastal zone

(http://tr.wikipedia.org/wiki/Marmara_B%C3%B6lgesi). In Fig. 1, the study area, sampling

locations and precipitation stations are showed by means of ArcGIS.
http://tr.wikipedia.org/wiki/Biga_%C3%87ay%C4%B1http://tr.wikipedia.org/wiki/Marmara_B%C3%B6lgesihttp://tr.wikipedia.org/wiki/Marmara_B%C3%B6lgesihttp://tr.wikipedia.org/wiki/Biga_%C3%87ay%C4%B1


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Fig.

1.

Mapofthestudyarea,

samplinglocationsandprecipitationstations


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3.2. Modelling Antibiotic Transport

3.2.1. GIS as a tool

In order to modelling the antibiotic transport in Marmara region, a GIS-based model will be

formulated. Geo-statistics methods will be used to build the relationship between the data

available and the data be obtained from GIS-based maps. In the first part of modelling study;

as can be summarized by Fig. 2, the aim of this study is to develop a conceptual model

including physical and logical components. The processes taking place in this conceptual

model will be brought into functional mode by using ModelBuilder component available in

the GIS software and the model will be worked.

Fig. 2: Antibiotic Application

The use of Model Builder enables following operations:

The results of model are monitored through ArcMap or ArcCatalog, Model simulates for variable parameters by changing parameter values, Desired number of data are added, Undesired process and data are removed.

3.2.2. Data Requirements

One of the most effective factors in the case of hydrological characterization of catchments is

to determine flow direction. Catchments are identified on the base of cells by means of

ArcGIS program scan pattern. Elevation and slope data are used with Flow Direction

function available in ArcGIS. As a result, cellular based flow direction data will be obtained

for all catchment.


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Since precipitation quantity is used as input data in the study, first step is to convert

precipitation into over-flow that calls as Rainfall-Runoff Modelling. In accordance with this

purpose, Spatial Analyst component of ArcGIS software will be used. In addition to annual

precipitation data, flow coefficients obtained from field and land use data belonging to

subcatchments will be used to compute over-flow quantities originating from precipitation.

Spatial analyst component will be used also for pollutant transport process which depends

upon over-flow. This process will be identified as following equation:

Runoff = Subcatchment Area x Annual Precipitation Quantity x Runoff Coefficient

All these steps create the conceptual model which can be illustrated as Fig. 3.

Fig. 3: Conceptual Model

For the pollutant transport process depending upon surface flow planned to be modelled,

Spatial Analyst and/or Particle Tracking tool taking place in Spatial Analyst will be used.

Model will use flow quantity found in previous stage and the concentrations determined for

each pollutant parameter. By doing this, the model will calculate pollutant load through

empirical methods for any catchment. Following equation will be used for this purpose:

Pollutant Load = Run-off x Estimated Mean Concentration (EMC)

Urine and faeces originating from animal excretion is responsible of antibiotic concentration

in soil. Due to antibiotics are poorly absorbed in the animal gut, residues can leach from dunginto soil. Highest concentration in soil is calculated as following equation (Montforts, 1999):

(2)

(1)


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InputQproduct: dosage product used [kg.kg bw

-1.d

-1] S

Cc: concentration a.i. in product [mgc.kg-1

] S

manimal: (averaged) body weight [kgbw.animal-1

] P

Ttreatment: duration of treatment [d] D

Fexcreted urine: fraction excreted in urine [-] S/D

Nanimalfield: stocking density animals [animal.ha-1

] P

RHOsoil: dry bulk density of soil [kg.m-3

] Dc

DEPTHfield: mixing depth with soil [m] Dc

CONVareafield: conversion factor for the area of the field [m2.ha

-1] D

c

RHOsoliddung: density of dung solids [kg.m-3] DcRHOwater: density of water [kg.m

-3] D

c

Fwaterdung: fraction water in dung [m3.m

-3] P

Fsoliddung: fraction solids in dung [m3.m

-3] P

Focdung: weight fraction of fraction organic carbon in dung [kg.kg-1

] Dc

Koc: partition coefficient organic carbon - water [dm3.kg

-1] S/O

Intermediate Results

Qexcreted urine: quantity a.i. excreted with urine [mgc.animal-1

] O

Qleached dung: quantity a.i. leached with dung [mgc.animal-1

] O

Fexcreted dung: fraction excreted in dung [-] O/SFleached dung: fraction leached from dung [-] O

Kdung-water: partition coefficient solids and water in dung [m3.m

-3] O

Kpdung: partition coefficient solids and water in dung [dm3.kg

-1] O

Output

PIECsoil: highest concentration in the soil [mgc.kgsoil-1

] O

3.3. Mapping the environmental Risk: A GIS-based Approach

In the scope of this study, it is intended to build a semi-distributed model. In the case of fully-

distributed or semi-distributed models, spatial parameters show different distributions from

region to region. Thus, the effects of pollutants will vary depending on the geographical

region in which they exist. With the aim of clarifying these differences, an Environmental

Risk Map based on pollutant transport in Marmara region will be constituted.

(3)


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For each compartment evaluated, after identifying catchment on cellular basis, a separate Risk

Characterization Ratio (RCR) is calculated for every single cell, based on the PEC/PNEC

concept (Montforts, 1999):

Input

PECcom predicted environmental concentration in compartment [mgc.kg-1

] or [mgc.l-1

] O

PNECcomp predicted no effect concentration for compartment [mgc.kg-1

] or [mgc.l-1

] O

Output

RCRcomp risk characterisation ratio for compartment [-] O

Risk factors for every single cell will be illustrated by means of GIS based maps. Main

parameters taken into consideration in the process of constituting these maps are cellular

space, arable field area, distance from water resources, population/residential density, and

slope, etc.

Through the amount of arable fied area in a given grid cell (x,y), gLOAD per grid cell is

calculated and the rule of proportion is applied as shown in Eq. (5) (OECD, 1998):

Astream i,j is the amount of arable land in the near upstream environment of a stream site

located in grid cell (x,y),

Acell i,jis the amount of arable land in cell (x,y),

Estream i,j is the theoretical size of the near-stream environment of the stream site located in

grid cell (x,y),

Ecellis the size of the grid cell (x,y).

Runoff Potential (RP) can be transformed into the estimated median effect value of a grid cell.

Data about catchment areas i.e. the frequency of stream sites gives the potential effect

frequency per grid cell. The median effect value multiplied by the effect frequency forms an

estimate of the environmental risk in a grid cell. Predicted environmental risk for study region

is calculated as following equation by taking into account the grid cells where stream sites

exist. (Schriever and Liess, 2007).

(5)

(4)


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index irefers to one of the environmental risk classes Very low to Very high,

mRiskcell(x,y) is the median (lower; upper) estimate of environmental risk for streams in gridcells that belong to risk class i,

n is the number of investigated sites that are located in grid cells of risk class i.

4. PROBABLE OUTCOMES

Once it is built, the model will be run under different scenarios to account for the changes in

the catchment area. Scenarios will be developed based on the application of 3 different types

of antibiotics in various dosages, in different seasons on different types of soil. If the study is

completed successfully:

For Marmara region, a GIS based data set including analyzed antibiotics collected fromvarious points, soil type, and land use data will be formed,

Hydrological model exhibiting rainfall-runoff relationship in the catchment alsothermodynamic and hydrodynamic models determining antibiotic transport will be built,

The effects of land use on rainfall-runoff and antibiotic transport will be evaluated, The variables and parameters that affect antibiotic transport will be determined and

significance levels will be researched,

Besides determining the variables and parameters creating model, the correlations witheach other will be clarified,

Environmental Risk Map for Marmara region depending on antibiotic transport will beillustrated.

(6)


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Blackwell, P.A., Kay, P., Boxall, A.B.A, 2007. The dissipation and transport of veterinary

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Boxall, A.B.A., Blackwell, P., Cavallo, R., Kay, P., Tolls, J., 2002. The sorption and transport

of a sulphonamide antibiotic in soil systems. Toxicology Letters, 131, 1928.

Branger, F., Tournebize, J., Carluer, N., Kao, C., Braud, I., Vauclin, M., 2009. A simplified

modelling approach for pesticide transport in a tile-drained field: The PESTDRAIN model.

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Chu, X., Marino, M.A., 2007. IPTM-CS: A windows-based integrated pesticide transport

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Davis, J. G., Truman, C. C., Kim, S. C., Ascough II, J. C., Carlson, K., 2006. Antibiotic

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http://tr.wikipedia.org/wiki/Marmara_B%C3%B6lgesi

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