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CHARACTERIZATION OF LEACHATE FROM MUNICIPAL SOLID WASTE(MSW) LANDFILL

Gunjan Bhalla 1, Arvind Kumar 1, Ajay Bansal 2

1 Department of Civil Engineering, Dr. B.R. Ambedkar, National Institute of Technology,Jalandhar-144011, Punjab, India.

2 Department of Chemical Engineering, Dr.B.R.Ambedkar National Institute ofTechnology, Jalandhar-144011, Punjab, India.

ABSTRACT

The paper discusses the characteristics of leachate generated from municipal solid waste landfillsite at Suchi Village, Distt. Jalandhar, Punjab. Leachate sample was collected and analyzed forvarious physico-chemical parameters to estimate its pollution potential. It has been found thatleachate contains high concentrations of organic and inorganic constituents. Hence liners must beused at the landfill site as a remediation step to prevent groundwater contamination.

Key words: Leachate, Suchi Village, Organic and Inorganic constituents, Groundwater contamination.

INTRODUCTION

With the rapid industrialization andpopulation growth, the status of ourenvironment is degrading day by day. As thelimits of urbanization are extending to farflying areas in India, the problem of solidwaste management is causing a greatconcern to our environment. Seeing thescenario of increase in generation,improper utilization and disposal of wastein the country, the Ministry of Environmentand Forest (MoEF) has developed “TheMunicipal Solid Waste (Management andHandling) Rules, 2000”, which states thatMunicipal Solid Waste (MSW) is commercialand residential wastes generated in amunicipal or notified areas in either solid orsemi-solid form, excluding industrialhazardous wastes but including treated bio-medical wastes. These solid wastes aregenerally disposed off in a low lying areacalled sanitary landfill area by the municipalauthorities. These rules have specifiedmany compliance for the management ofsolid waste for the State Committee andPollution Board, which includes proper

segregation of solid waste intobiodegradable waste, recyclable and othersi.e., non-recyclable wastes are stored incolored bins (Green for biodegradablewaste, blue for non-biodegradable wasteand black for hazardous waste) at thesource of generation and properly treated,recycled and disposed to landfill areas.Solid waste management in India hasalways been considered a low priority area(Kansal, 2002). In India more than 90% ofthe municipal solid waste is disposed offby landfilling (Holmes, 1984). Today morethan 45 million tones/year of solid waste isgenerated from the urban centres of India,which are collected inefficiently, transportedinadequately and disposed unscientifically(TERI, 1998). The generation is expected torise to 125 million tones/ year by the year2025 (Shaleen and Suneel, 2001).Accordingto Ministry of Urban Affairs, Govt. of Indiaestimate, India is generating approximately100,000 metric tones of solid wasteeveryday of which 90 % is dumped in theopen place (The Expert Committee, SolidWaste Management, 2000). In Delhi, thecapital of India alone, more than 5000

ECO-CHRONICLE, Vol.3., No. 4.December 2008, pp: 231 - 237

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tonnes of Municipal Solid Waste (MSW) isgenerated everyday and is expected to riseto 12750 tonnes per day by 2015(Ahsan,1999). The MSW generated per dayin India’s other major cities are Mumbai-6050 tonnes, Kolkata -3500 tonnes,Chennai-2500 tonnes, Bangalore-2000tonnes, Hyderabad- 1800-2000 tonnes,Lucknow-1500 tonnes and Ahmedabad-1280 tonnes (The Expert Committee, SolidWaste Management, 2000).

Laws for Management of MSW

Prior to 1974 certain laws at regional andnational level were there to punish theoffender for making nuisance in publicplaces and pollution of water bodies. Evenin 300-400 B.C. in Arthasastra of Kautilya(Chanakya) provisions were there to punishoffenders for making nuisance in publicplaces but these were either ineffective ornot strictly enforced. Even the EnvironmentProtection Act, 1986 was silent in solid wastemanagement and the Govt. of India’sconsciousness is mostly after U.N.declaration and declaration by somedeveloped countries. Laws pertaining toSolid Waste Management (SWM) since 1974are as enumerated below (Bisoyi, 2005).

Post independence period

1974 Water (prevention and control ofpollution) Act -amended in 1978 and 1988.1981 Air (prevention and control ofpollution) Act -amended in 1987.1986 Environment Protection Act(umbrella act) even was silent in MSWmanagement.1989 Hazardous waste managementand handling rule.1990 Govt. of India and Supreme Courtinstigated on the necessity of solid wastemanagement.1998 Bio-medical waste (Managementand handling) rules amended in 2000.1999 Recycled plastic manufacturedand usage rules.1999 Solid waste management inClass-1 cities in India-guidelines bySupreme Court of India.

2000 Municipal waste (Managementand Handling Rules).

In Punjab, growth of population,industrialization and urbanization hasresulted in generation of large volumes ofsolid waste. Most of the solid wasteis presently disposed off on land andremains uncovered result ing inenvironmental pollution of surroundingareas. Increased accumulation of solidwastes is creating economic andenvironmental problems in the state, dueto decreased availability of land for disposal.While municipal and industrial solid wastehas attracted the attention of the authorities,yet there is lack of concern for some specialmanagement of biomedical wastegenerated primarily from hospitals andother health care centers in the state. Sothere is an urgent need to treat all wastesas resource material for recycling. Somefor conversion to fertilizers or as source ofenergy and rest for land reclamation.Population growth, rapid urbanization andother development activities during the pastfew decades have been responsible forenvironment pollution and resourcesdegradation. The rapid urbanization hasseriously aggravated the problem ofmunicipal or domestic garbage disposal &management. (Punjab State Council forScience and Technology (PSCST, 2005).A large volume of domestic solid waste isgenerated in both urban as well as ruralareas of Punjab. As per Punjab PollutionControl Board (PPCB, 2006), a total of3034.65 tons per day of solid waste is beinggenerated in municipal areas includingCantonment boards. Table 1 showsgeneration of MSW in major cities of Punjab(PPCB, 2007). However, all municipalbodies do not have adequate infrastructurefor handling the same. The life of existinglandfill sites is also expected to be reducedwith increase in volume of wastegeneration. The major waste is from classI cities as more than half of the state’s urbanpopulation (58.39%) lives in these cities.Further, out of total municipal solid wastegeneration, 71% of waste is from the fivecorporations (Ludhiana, Amritsar,

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Jalandhar, Patiala and Bathinda).TheLudhiana alone generates 31 % of totalMSW generated in the state.

As per PSCST (2005) report, the urban localbodies spend approximately Rs.500 toRs.1500 per ton on solid waste collection,transportation, treatment and disposal.About 60-70% of this amount is spent oncollection, 20-30% on transportation andless than 5% on final disposal. However,infrastructure available with municipalcorporations for collection, transportationand disposal/management is limited andmostly less than the required level forproper handling of wastes. As a result,waste is not collected from the entire city ona daily basis and goes on accumulating atprimary dumping sites.

In urban Punjab, municipal solid waste isgenerally dumped outside the houses,shopping centre, offices, streets, etc or atsome collection sites and is left formunicipal authorities for taking it to acommon dumping ground. It is common tofind solid waste dumps near towns andcit ies. These dumps are mostly indepression or in open grounds.Widespread water, air and land pollution iscaused from these dumps. The dumpingsites are not properly managed nor havebeen planted with suitable plant species tohelp in quick degradation of solid waste byway of creating conducive for the growth ofmicroorganism besides providing greenery.Appropriate post dumping practices arealso seldom performed causing perpetualproblem of air and water pollution. A numberof incidents have been reported where solidwaste leachates contaminated thesurrounding soil and polluted the

Corporation Population in lacs MSW in TPD Per capita per daygeneration in grams

Patiala 3.23 180 560Ludhiana 13.95 850 610Jalandhar 8.5 350 450Amritsar 9.75 450 460Total 33.94 1830 2080

Table: 1 Generation of MSW in major cities of Punjab

TPD: Tonnes per day

underlying groundwater aquifer or nearbysurface water bodies (Chain and Dewalle,1976, Walker, 1969, Masters, 1998). TheMSW exert specific environment and healthimpacts including spread of epidemics andtherefore, required to be properly managedand disposed. Municipal Solid Waste is,however, not being appropriately manageddue to inadequate finances, inadequatetraining of personnel, lack of performance,monitoring, inadequate emphasis onpreventive maintenance, etc. At presentmost of the solid waste is being disposedoff in an unscientific manner.

There are 12729 vi l lages in Punjabaccording to 2001 census. In rural Punjabgarbage, which includes household waste,cattle dung, agro waste, etc, is beingmanaged by personal efforts of theresidents. The household waste, as wellas cattle dung is generally collected outsidethe house/ village at earmarked places forthe whole year. The most part of the cattledung is made into dung cakes for use asfuel, the rest is collected for subsequent usein agricultural f ields as manure. Thecharacteristic feature of rural solid waste isthat it is generally free from glass, metal orother non-biodegradable material.Although this garbage is getting managedyear after year, still there is a lot of scopeand need for improvement. The garbageand dung pits create an unpleasant sightand odour if not covered properly andcontribute to slush during rainy season.

Table 2 and 3 shows the Physical andChemical composition of the MunicipalSolid Waste (MSW) generated in Punjab(PPCB, 2003).

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In the present study, the experimental workis carried out to ascertain the compositionof leachate generated from municipal solidwaste dumping site at Suchi Village, districtJalandhar near National Highway No.1 witha view to estimate its pollution potential.

Study Location

Jalandhar is a major city of Punjab with apopulation of more than 8 lakhs lying atlatitude 31.33° N and longitude 75.58°E withan average elevation of 229 m. At presentthere are two designated municipal solidwaste dumping sites available at Jalandhar.Leachate sample for present study iscollected from municipal solid wastedumping site at Suchi Village; districtJalandhar near National Highway No.1spreads over 2 acres of low lying land area.This site is operational since 2004, receivingnon-hazardous municipal waste. The siteis non-engineered low lying open dump,looks like a huge heap of waste up to aheight of 6–10 m. A variety of vehiclesdelivers the waste to the site resulting in awide range of unloading procedures like

Category Item Percentage

Paper, Plastic, Rags 3-5

Leather, Rubber, Synthetic 1-3Glass, Ceramics 0.5-1

RecyclableMaterial

Metals 0.2-2CompostableMaterial

Food articles, Fodder, Dung, Night Soil,Leaves, Organic Material

40-60

Inert Material Ash, Dust, Sand, Building Material 20-50Moisture 40-80Density 250-500 kg/m

3

Table: 2 Physical Composition of MSW generated in Punjab

Table: 3 Chemical Composition of MSWgenerated in Punjab

Item Percentage

Nitrogen 0.56-0.71Phosphorus 0.52-0.82Potassium 0.52-0.83C/N 21-30Calorific value 800-1010 Kcal/Kg

end tipping, side tipping and manualunloading. Unload waste is tipped in conicalpiles and then spread out by bulldozers. Nocover of any description is placed over thespread waste to inhabit the ingress ofsurface water or to minimize litter blow andodours or to reduce the presence of verminand insects. Rag pickers regularly set fireto waste to separate non-combustiblematerials for recovery. Since, there are nospecific arrangements to prevent flow ofwater into and out of landfill site, the diffusionof contaminants released duringdegradation of landfill wastes, may proceeduninhibited.

MATERIALSAND METHODSLeachate sampling and analysis

Leachate sample was collected duringrainy season. The landfill site was notequipped with a leachate collectors.Leachate sample was collected from thebase of solid waste heaps where theLeachate is drain out by gravity. To determinethe quality of leachate, integrated sampleswere collected from randomly selectedlocations. The samples were collected in awell- labeled clean bottles that were rinsedout thrice prior to sample collection.

Analytical Work

Analytical methods were according to “Standard methods for examination of waterand wastewater” (APHA, 1998). The pH wasmeasured by electronic pH meter (digitalpH meter 5652). (4500-H+.B of Standard

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Methods). Turbidity of a sample wasmeasured by Nephelometer by usingoptical properties of light. (2130 .B ofStandard Methods). Properly shakedunfiltered sewage was used and estimatedby gravimetry. (2540 .B of StandardMethods). Fi l tered sewage throughwhatman filter 44 enables to determine TotalDissolved Solids (2540.C of StandardMethods). Total Suspended Solids wasdetermined by the difference between totalsolids and total dissolved solids (TS-TDS).(2540.D of Standard Methods).Argentometric volumetric titration method inthe presence of Potassium chromateprovides reliable results of chloride (4500-Cl-.B of Standard Methods). Total Hardness-EDTA titration method with presence of EBTindicated was adopted (NEERI, 1981).Chemical Oxygen Demand (COD)- refluxionof sample followed by titration with FAS wasadopted. (5220-C of Standard methods).

Dissolved Oxygen (DO) was determined byAzide modification of Winkler`s method.Biological Oxygen Demand (BOD)-Themethod of winklers was used for estimatinginitial and final D.O. in the sample and BODwas determined (5210-B of Standardmethods). Amm. Nitrogen, Phosphate, Iron,Lead, Chromium hexavalent & Cadmiumwas estimated using UV-VISSpectrophotometer.

RESULTS AND DISCUSSION

Landfill that receives a mixture of municipal,commercial, and mixed industrial waste, butexcludes signif icant amounts ofconcentrated specific chemical waste,landfill leachate may be characterized as awater-based solution of four groups ofpollutants (dissolved organic matter,inorganic macro components, heavy metalsand xenobiotic organic compounds(Christensen et. al., 1994).

Sr.No.

Parameters Results Standards ( Mode of Disposal )*

Inlandsurfacewater

Publicsewers

Landdisposal

1 Appearance Brownish - - -2 Odour Sewage smell - - -3 pH 10.3 5.5 to 9.0 5.5 to 9.0 5.5 to 9.04 TS mg/l 8600 - - -5 SS mg/l 1800 100 600 2006 TDS mg/l 6800 2100 2100 21007 Hardness mg/l 638 300 - -8 Turbidity NTU 30 5 10 109 BOD (3 days at

270 C) max. (mg/l)

809 30 350 100

10 COD mg/l 1690 250 - -11 Chloride mg/l 853 1000 1000 60012 Amm Nitrogen mg/l 83 50 50 -13 Phosphate mg/l 78 - - -14 Iron mg/l 6.6 0.01 0.01 -15 Lead mg/l 0.9 0.1 1.0 -16 Chromium

hexavalent mg/l1.5 2.0 2.0 -

17 Cadmium mg/l 3.2 2.0 1.0 -

Table 4 Physico-chemical characteristics of the leachate

* Municipal Solid Wastes (Management and Handling) Rules, 2000

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Leachate is the liquid residue resulting fromthe various chemical, physical andbiological processes taking place within thelandfill. Landfill leachate is generated byexcess rainwater percolating through thewaste layers in a landfill. A combination ofphysical, chemical, and microbialprocesses in the waste transfer pollutantsfrom the waste material to the percolatingwater (Christensen and Kjeldsen,1989).Bagchi (1989) described typicalcontaminants present in landfill leachate.Similarly, Lee and Jones (1993b) describedtypical composition of municipal landfillleachate. The composition of landfi l lleachate, the amount generated and theextraction of potential pollutants from thewaste depend upon several factors,including waste composition, degree ofcompaction, absorptive capacity of thewaste and waste age, the climate, levels ofprecipitation, Landfill temperature, size ,geology, engineering and operationalfactors of the landfill (Leckie and Pacey,1979; Kouzeli-Katsiri et al.,1999).

The results of leachate analyzed for variousphysico-chemical characteristics and alsostandards for the discharge of treatedleachates on Inland surface water, Publicsewers and Land disposal were as shownin Table 4. The results indicate that most ofparameters of leachate were beyondpermissible limits.

SUMMARY AND CONCLUSION

It has been concluded that since leachatecontains high concentrations of organic andinorganic constituents and heavy metals,liners must be used at the dumping site.The presence of hand pumps near thelandfill site to draw groundwater threatensto contaminate the groundwater, andimmediate remediation steps should betaken at landfill site.

REFERENCES

Ahsan Naved, 1999. Solid WasteManagement Plan for Indian Mega Cities,Indian J.Env. Prot., Vol.19, (2), pp: 90-95.

APHA.,1998. Standard Methods forExamination of Water and Wastewater.19th

edition, American Public Health Association,American Water Works Association, WaterEnvironment Federation Publication,Washington, DC.

Bagchi,A.,1989.Design, construction andmonitoring of sanitary landfill. JohnWiley &Sons, NY.

Bisoyi, L.K., 2005. Status of Solid WasteManagement (SWM) in Puri Municipality,Puri. Govt. of Orissa, pp: 91-95.

Chain, E.E.K and Dewalle, F.B., 1976.Sanitary landfi l l leachates and theirtreatment, ASCE, Journal of EnvironmentalEngineering Division, 102(2), pp: 411-431.

Christensen, T.H. and Kjeldsen, P., 1989.Basic biochemical processes in landfills.Chapter 2.1 in Sanitary Landfilling: Process,Technology and Environmental Impact,Christensen, T.H., Cossu, R and Stegmann,R., Eds., Academic Press, London, UK, pp:29.

Christensen, T.H., Kjeldsen, P., Albrechtsen,H.J., Heron, G., Nielsen, P.H., Bjerg, P.L., andHolm, P.E., 1994. Attenuation of landfillleachate pollutants in aquifers, Crit. Rev.Environ. Sci. Technol., (24), pp: 119.

Holmes John, R., 1984. Management ofSolid Waste in Developing Countries, JohnWiley and Sons Limited.

Kansal, A .,2002. Solid Waste ManagementStrategies for India, Indian J. Env. Prot, 22(4),pp: 444-448.

Kouzeli-Katsari, A.,Bodogianni A.,ChritoulasD.,1999. Prediction of leachate quality fromsanitary landfills, Journal of EnvironmentalDivision, ASCE,125 (EE10), pp: 950-957.Leckie, O.J, Pacey, J.G., 1979. LandfillManagement with moisture control, J. ofEnvironmental Engineering Division, 105(EE2), pp: 337-355.

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Lee, G.F. and Jones-Lee, A., 1993b.Groundwater pollution by municipal landfills:Leachate composition, detection and waterquality significance, International LandfillSymposium, Sardinia, Italy, pp: 1093-1103.

Masters G.M., 1998. Introduction toEnvironmental Engineering and Science,Prentice- Hall of India Private Limited, NewDelhi.

MSW Rules, Municipal Solid Wastes(Handling and Management) Rules, 2000.Ministry of Environment and Forests,Gazette of India, 2000.

NEERI., 1981 . Manual on water andwastewater analysis, Nagpur. pp: 57- 309.Shaleen Singhal and Suneel Pandey, 2001.Solid Waste Management in India: Statusand future directions, TERI InformationMonitor on Environmental Science, 6(1), pp: 1-4.

Status report on Municipal Solid Waste(MSW) by Punjab Pollution Control Board(PPCB, 2003; 2006; 2007), Patiala.

TERI, 1998. Looking Back to Think Ahead-Green India 2047, Tata Energy ResearchInstitute, New Delhi, pp: 346.

The Expert Committee, 2000. Manual onMunicipal Solid Waste Management, TheExpert Committee constituted by Ministry ofUrban Development, Government of India.Walker, W.H.,1969.Illinois Ground waterpollution, Journal of American Water WorksAssociation, 61, pp: 31-40.

Report on Solid Waste Management byPunjab State Council for Science andTechnology (PSCST), Chandigarh incollaboration with Punjab Pollution ControlBoard (PPCB), Patiala, www.punenvis.nic.in/swmgmt_domestic.htm.

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ECOLOGICAL IMPACT OF LULC DYNAMICS IN THE FORESTED LANDSCAPEOF THE BETLA NATIONAL PARK

Arabinda Sharma 1 and Nilanchal Patel 2

1 Agricultural and Food Engineering Department, Indian Institute of Technology,Kharagpur, West Bengal.

2 Remote Sensing Department, Birla Institute of Technology, Mesra,Ranchi, Jharakhand.

ABSTRACT


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Landscape modification and subsequent loss of forest continue to be the major factor affectingquality of wildlife habitat. The spatial pattern of Land Use and Land Cover (LULC) has a directecological implication on resource management and wildlife habitat quality. This paper attempts toquantify the spatial pattern of landscape using different pattern metrics and develops an approachfor monitoring the influence of landscape dynamics on wildlife habitat quality over a period of onedecade. Indian Remote Sensing (IRS) satellite data of the year 1990 and 2000 were used togenerate thematic LULC maps though a hybrid classification method. Different landscape levelmetrics such as diversity, contagion, fractal dimension, fragmentation and connectivity werecalculated using ArcInfo software and special computer program written in C language to characterizethe change in landscape pattern. A compound Habitat Quality Index (HQI) was also developedthrough linear combination of calculated landscape metrics to quantify the habitat quality. Thevalues of HQI were found to be 0.4051 and 0.3762 for the year 1990 and 2000 respectively. Thisindicated a deterioration of about 7.14% in habitat quality of the landscape dynamics over the studyperiod. Despite of intense landscape dynamics only a marginal deteriorate was observed in foresthabitat quality due to proper management practices taken by the forest officials to nullify negativeeffect of increased landscape dynamics.Keywords: Remote sensing, GIS, spatial pattern, landscape metric, habitat quality index

INTRODUCTION

The world-wide loss of forest land and itsdegradation due to intense landscapemodification is one of the major reasons ofloss of biodiversity. However, increasingpublic interest in biological conservationand legislated requirements for resourceimpact assessments seek for evaluatingwildlife habitat quality in response to landmanagement (Emlen and Pikitch, 1989). Thegrowing intensity of land use dynamics andits spatial pattern influence the speciespopulations by altering the quality of specieshabitat (Riiters et al., 2000). Landscapeecology hypothesizes that spatialarrangement has ecological implicationsand is important in assessing the status of

a variety of organisms that would not beevident from a land cover map otherwise.The first step to understand the interplaybetween landscape patterns and ecologicalprocesses is the description andquantification of spatial and temporalpattern (Hargis et al., 1997). Consequently,conservation strategies should consider notonly amounts of habitat to be retained, butalso their spatial configuration (Schumaker,1996).

Recent advances in the field of landscapeecology have included the development andapplication of quantitative approaches tocharacterize landscape condition andprocesses (Turner et al., 2001). Spatialpattern metrics provide quantitative

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descriptions of the spatial composition andconfigurations of habitat or land use andland cover (LULC) types. They can be usedas useful indicators of the habitat quality,ecosystem function, and the flow of energyand materials within a landscape.Landscape metrics have been used tofocus on colonization and extinction in ametapopulation (Mild´en et al., 2006), effectof endogenous competition and resourcedistribution on community assembly(Reineking et al., 2006), ecosystem/landscape functioning or ecosystem/landscape services (Turner, 2005a),comparison of ecological quality acrosslandscape scale (Frohn, 1997).

Most of the wildlife resource planningmodels put emphasis on developingdistribution or abundance patterns of asingle species (Walker 1990). However,there is a growing appreciation of thedynamic processes at the landscape scalethat involves community perspective ofhabitat and species diversity management(Zonneveld and Forman, 1990). Atlandscape scale, community or diversityproperties is comprised of a mosaic of landtypes which has become the focus in habitatassessment studies. Hence, this paper wasaimed to examine the influences oflandscape dynamics on forest habitat overa period of ten years. The specific objectivesof this paper are: 1) to quantify the landscapecharacteristics of the study area usingvarious landscape metrics and 2) todetermine the impact of change in LULCpattern on the overall habitat quality of thelandscape in a span of ten years i.e. 1990to 2000 through both pixel and patch basedapproaches.

MATERIALSAND METHODS

Study Area

The work was conducted in the forestedlandscapes of the Betla National Park,situated in Palamau district of the state ofJharkhand, India. The study area is extendedwithin the longitude of 840.13’E to 840.28’Eand latitude of 230.80’N to 230.95’N. The

climate of the area is monsoonal, receivingan annual rainfall of 600-1100mm. Theminimum and maximum temperatures ofthe area are 30 C and 450 Celsiusrespectively. The study area is dominatedby mixed tropical moist deciduous forest.The study area is also home to largenumber of endangered flora and fauna withvery rich biodiversity.

Approach

The most common approach to characterizethe landscape structure is to map thedefined landscape classes (e.g., habitattypes) using digital classification of satelliteimagery followed by delineation of patchesfor each landscape class. Patches aredefined as contiguous areas ofhomogenous landscape condition. Weused IRS 1C LISS III imagery of 26.11.2000and IRS 1B LISS II imagery of 16.12.1990 tocreate land use land cover (LULC) maps.The spatial resolution of LISS III and LISS IIare 36.5 m and 23.5m respectively. Surveyof India (SOI) toposheets 73 A/1 &73 A/5 ofscale 1:50000 were used assupplementary data in this study.

Preprocessing & LULC map generation

Satellite images obtained from the twosatellites were preprocessed for theirgeometric and radiometric behavior toensure their f ideli ty and to minimizeuncertainty in the output LULC map fromthe two images. Once the necessarycorrections were applied to the images,Anderson level-I Land use land cover(LULC) map (Anderson et al., 1976) wereprepared for the both year using digitalclassification. We assigned land coverclasses to the image using a combinationof supervised and unsupervisedclassification techniques. Initially, maps of20 spectral clusters were generated usingunsupervised classification (ISODATA) andthe spectral signatures of clusters werestored. Each spectral cluster was thenassigned a class name based on visualinterpretation of standard FCC image,ground truth data collected during field visit

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and comparing topographic maps. Thesignature of spectral classes that werefalling in same thematic classes weremerged and was later on used forgenerating final land use land cover mapthrough maximum likelihood algorithm ofsupervised classification. In the presentstudy seven thematic classes namely (1)dense forest, (2) moderate forest, (3) openforest, (4) water body, (5) barren land, (6)crop land and (7) settlement were identified.Since LISS II comes with 36m spatialresolution, LULC map year 2000 generatedusing LISS III data was resampled fromoriginal 23.5m resolution to 36m resolutionfor better comparison.

Characterizing Landscape Pattern

Both land use maps were then imported toArcGIS domain through image to gridconversion for landscape analysis. TheArcgrid file was also converted to ASCIIformat for computing some landscapemetrics using computer program written inC. Landscape metric such as Diversity(Shannon & Weaver, 1949), Contagion(O’Neill et. al., 1988), Fractal Dimension(Baker and Kai, 1992), Fragmentation(Civco et al, 2000), l ikeness index,Connectivity (Harris & Sanderson, 2002)were selected in the present study tocharacterize the landscape.

Habitat Quality Assessment

To model the habitat quality a simple modelhas been adopted which is based on theTheory of Insular Biogeography (Harris &Sanderson, 2000). According to this theoryhabitat refers to those land cover typesacceptable to a particular species in a broadsense and a species utilizes various landcover differently. Cover types may becategorized into different habitat type basedon the degree of suitability for a group ofspecies and spatial rule. The differenthabitat types are i) Optimal- habitat is theprime habitat ii) Sub optimal- habitat ishabitat, that is less than the optimal one,perhaps where reproductive and foragingsuccess are high but not optimal i i i)

Marginal-habitat refers to the habitat wherespecies can survive, but might notadequately reproduce iv) Detrimental habitatis one which conceptually influences thehabitat of the species in negative manner v)Nontraversible- habitat is that which is nontraversible and act as barrier vi) Inevasible-habitat is one not currently occupied, butcould if condition changes. Rulessuggested for classifying a cell into a habitattypes is as follows:

Rule 1: Detrimental cell reduces theirneighbor optimal cell to marginal.Rule 2: Marginal cells reduce their optimalneighbor cell to suboptimal.Rule-3: Suboptimal cells have damagingedge effect on neighboring optimal cells.Rule-4: Marginal cells create edge effect inneighboring suboptimal cell.

All the LULC types in the present study werecategorized into one of the above habitattypes based on the intensity of human activityor disturbance level. The dense, moderateand open forests were categorized intooptimal, sub-optimal and marginal habitattype respectively. The water bodies andbarren land were considered as invasiblehabitat type while crop land and settlementwere considered as detrimental habitat type.Thus, the number of pixel under eachhabitat type based on the above defined rulecan be obtained as follows:Number of optimal habitat pixels= N1 – C13 – 2 (C16 + C17) - C12Number of optimal habitat pixels with edgeeffect = C12Number of sub-optimal habitat pixels= (N2 + C13 + C16 + C17) - (C26 + C27) – C23Number of sub-optimal habitat pixels = C23Number of marginal habitat pixels= N3 + C16 + C17 +C26 + C27Number of invasible habitat pixels = N4 + N5Number of detrimental habitat pixels = N6 + N7

Where, N1, N2, N3, N4, N5, N6 and N7 are thetotal number of pixels of dense forest,moderate forest, open forest, water, barrenland, crop land, and settlement respectively.While C13, C16, and C17 represent number ofadjacent pixels of dense forest with open

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Class Name Classcode

Habitat type Weight assigned

Dense Forest 1 Optimal 1.00 (0.95 for cell with edge effect- C12)Moderate Forest 2 Sub-optimal 0.75 (0.70 for cell with edge effect -C23)Open Forest 3 Marginal 0.50Water Bodies 4 Invasible 0.25Barren land 5 Invasible 0.25Crop land 6 Detrimental 0.00Settlement 7 Detrimental 0.00

Table 1. Habitat types and weight assigned to each LULC types

forest, with cropland and with settlementrespectively, and C26 and C27 represent thenumber of adjacent pixels of moderateforest with cropland and with settlementrespectively.Once each cell has been assigned with ahabitat type, the habitat quality of thelandscape can be quantified using thefollowing formula.HQI = (“ wi * ni ) / NWhere, N = total number of pixels of allhabitat types including detrimental. While,ni and wi are number of pixels in ith habitattype and weight assigned to ith habitat type.The different habitat types were assigneddifferent ranging from zero for detrimentalhabitat to one for optimal habitat based ontheir relative naturalness, relativeimportance in providing shelter to wildlifeand disturbance intensity. The final habitatassignment to each of the LULC classesand their corresponding weights arepresented in Table 1.

During calculation of number of pixels of ahabitat type, the rules of juxtaposition andedge effect which are described in detail insection 2.2.3 were kept in consideration.Temporal change in habitat suitability wasobtained by simple differencing of HQIobtained for year 1990 and 2000.

RESULTSAND DISCUSSION

Preprocessing and Classification

The reliability of any landscape analysisdepends on the accuracy of thematic mapbeing used for the calculation of variousmetrics. Thus, the initial step of the above

task was to generate a good quality thematicmap showing the various landuse/landcover present in the study area. To ensurespectral and spatial homogeneity, requiredduring temporal analysis of remote sensingdata, necessary geometric and radiometriccorrection of the satellite images werecarried out. This along with hybridclassif ication approach helped us forreducing the problem of spectraloverlapping of the various landuse classes.The overall classification accuracy wasfound to be 85.4% for year 1990 while it was89.75% for the year 2000 respectively. Kappacoefficient was also found to be better forthe year 2000 (0.86) than the year 1990(0.81). The landscape composition for studyperiod was described using area as wellas patch statistics of different thematicLULC classes (Table 2).

Forest classes are continuing to be thedominant land cover classes during thestudy period. Forest classes collectivecover 17736.41 ha (50.49%) and 16198.96ha (46.12%) in the year 2000 and 1990respectively. Thus, there is a net increasein the 1537.44 ha (4.38%) in total forest coverin the span of ten year. Within the forestclasses, the aerial coverage is found to bein the order of dense forest > moderatedense > open forest. The aerial extent ofdense forest is found to be decreased (4.94%) while a gain of 2.26 % and 7.05 % hasbeen observed for moderate and open forestrespectively during the study period. Cropland is found be the single most dominantland cover classes in study site due tointensive agricultural activity practiced by theclusters of villages adjacent to National

243ECO-CHRONICLE

IRS LISS III (2000) IRS LISS II (1990)LULC classNo ofpatch

Area(Ha)

% No ofpatch

Area (Ha) %

Dense Forest 1803 6530.15 18.54 210 8264.07 23.53Moderate Forest 3656 5768.37 16.42 766 4973.92 14.16Open Forest 4946 5437.89 15.48 602 2960.97 08.43Water 933 921.72 02.62 677 1661.73 04.73Barren land 707 424.05 01.21 702 2641.51 07.52Crop land 1671 13351.39 38.01 402 13797.99 39.28Settlement 4670 2692.57 07.67 560 825.94 02.35

Table 2. Compositional Statistics of different LULC classes

Metric LISS-III (2000) LISS-II (1990)Diversity 0.8791 (1.7496) 0.9688 (1.8853)Dominance 0.0933 0.1586Contagion 0.2270 0.3019fractal dimension 0.8359 (1.6719 ) 0.6377 (1.2755 )fragmentation 0.4537 0.4410Likeness index 0.1801 0.1443Connectivity 0.4951 0.4026

Table 3. Value of different Landscape metric for the studied period.

Note: value in bracket shows the actual value computed for the metric which been scaledbetween 0 to 1.

park. An area of about 13351.39 ha(38.01%) and 13797.99 ha (39.28%) arefound under agricultural activity in the year2000 and 1990 respective. Settlementoccupies 7.67 % of the study site during2000 which more than three time its previousvalue (2.35 %) in the year 1990. But there islittle scope in the study site for such drasticincrease in settlement class. Hence, apossible chance of misclassification cannot be ignored. It is also supported byconfusion matrix of the two classified map.In case of 1990 LULC map, 16.67% trainingarea for settlement has been misclassifiedas crop land and in 2000 a net of 2.6% ofwater pixel are found to be included insettlement. Similarly for water class, adecrease of 2.11% has been observedwhich may either be attributed to rainfallvariability or low classification accuracy forwater class in 1990 LULC map. The barrenland which mostly comprising of degradedforest has been reclaimed and hence adrastic decrease has been observed in thestudy period. There is a remarkable

difference in the number of patches ofdifferent LULC class between the studiedyears. In case of 2000, the number ofpatches is much higher as compared to1990 for all LULC classes. This differenceis not only due to fragmentation but alsocontributed by differences in the spatialresolution of the images used in the studiedwhich ult imately affected the patchdelineation.

Changes in Landscape Pattern

The spatial pattern metrics express thecomplexity and variability among the landclasses occurring on a landscape andsuccessful description of pattern change isa critical component of habitat analyses. Thevalues of the different metrics are presentedin Table 3.

Shannon Diversity index is the measure ofthe richness measured in terms of types orthe variability and evenness measured interms of the relative abundance of the

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species or land use classes. Dominancemeasures the extent to which one or moreclasses dominate the landscape. Theactual values of diversity indices werescaled between 0-1 by dividing the actualvalue by the theoretical maximum value forsail number of classes. Diversity index forthe 1990 data was found to be greater thanthat of 2000 data. Since diversity is a positiveindicator of the habitat quality and stability,it can be inferred that there is decrease inthe habitat quality in terms of diversity index.Theoretically, dominance is inverselyproportional to diversity i.e. if diversity ismore, then dominance will be less and vice-versa. One of the important observations isthat decrease in the diversity value causedproportionately high increase in thedominance of the study area which iscertainly an indicator of degrading habitatquality.

Contagion index indicates how the differentland use classes are clumped together. Thecontagion values for each time period is lowindicating the presence of a large numberof small patches. Since contagion isinversely proportional to the fragmentation,it can be inferred that there is a large degreeof fragmentation in the landscape. Although,2000 data have more number of patches(large fragmentation) than 1990 data, it hascontagion greater than the 1990 data. It canbe explained on the basis that the contagionindex not only depends on the number ofadjacency value but how they are distributedamong the various class pair. Inspite of thelarge number of fragments the value ofcontagion was high due to equal distributionLULC edges between all the class-pairs.The contagion value in pixel approach isgreater than the patch approach indicatingthat decrease in the size of the measuringunit brings uniformity in the distribution oftotal adjacency. From the above discussionon the contagion value it can be concludedthat contagion provides meaningfulinformation regarding the fragmentation ofthe landscape but in qualitative terms.

Fractal dimension is used to measure thepatch shape complexities. The value of

fractal dimension is greater for 2000 dataas compared to 1990 data. Fractaldimension combines the concept of thefragmentation and edge amount. The valueof fractal dimension of a landscape variesaccording to the type of land use (O’Neill etal., 1988). For example, forested area(natural) tends to have more complexshape and manifest high fractal dimension,while the agricultural land (man-made)have simple shape and thus have a lowfractal dimension. There also appears tobe correlation between fractal dimensionand the degree of human disturbances inthe landscape. Thus increased fractaldimension value indicates a decrease inprevailing disturbances and controlledhuman activity in the landscape. Fromconservation point of view, disturbance fromthe adjoining area has decreased and thereis an increase in habitat quality.

During calculation of forest fragmentationindex, we counted the number of forest pixelwithin a window by excluding the water pixelfrom the landscape. Because, naturallyoccurring water bodies are not the agent offragmentation, at least when one’s aim isto measure the human caused LULCdynamics. Fragmentation increasesvulnerability towards external disturbancesand threatens the existence of patch survivaland associated biodiversity. It has negativeinfluences on edge-sensit ive interiorspecies and large carnivore like tiger. Thislast effect is the key starting point of manyfurther environmental degradation anddisturbances. The value of forestfragmentation index of 2000 data is greaterthan that of the 1990 data. The high forestfragmentation value for 2000 is alsosupported by high patchiness in forest classin 2000 as compared to 1990 (Table 1).However, this may not be sole because ofactual fragmentation but the exclusion ofdiagonally contiguous pixel in patchdefinition and difference in spatial resolutionof imagery being used in respective years.

Likeness is a quantitative metric formeasuring the quality of associationbetween habitat patches and is very much

245ECO-CHRONICLE

2000 1990Habitat type Weight(w) No of Pixel

(n) n*w No of Pixel

(n) n*w

Optimal 1.00 12563 12563.00 51288 51288.00Optimal * 0.95 37590 33831.00 12010 10809.00Sub-optimal 0.75 19957 14967.75 29437 22077.75Sub-optimal * 0.70 23122 16185.40 5944 4160.80Marginal 0.50 43623 21811.50 26313 13156.50Invasible 0.25 10384 2596.00 33204 8301.00Detrimental 0.00 123796 0.00 112839 0.00Total 271035 101954.65 271035 109793.05HQI 0.3762 0.4051

Table 4. Number of pixel under each habitat type, their weight and HQI for study period

Note: * indicates habitat type with edge effect

similar to Similarity index (McGarigal &Marks, 1995). It is important for thosespecies, which use specif ic ecotone(transition zone between two differentlanduse classes or habitats) as their habitat.The greatest biodiversity is obtained wherethere is an optimal blend of patches andecotone. When a landscape ischaracterized by large-sized patches, thenumber and ecotone are expected to be low.In this landscape biodiversity will be low. Incontrast, if the landscape is highlyfragmented the inner edge-sensit ivespecies would suffer. Thus, a spatialbalance of a landscape by contrasting thenumber of forest openings with the numberof existing patches is required. Therefore,during the calculation of Likeness indexweights were assigned to a patch pairaccording to their naturalness. Highlikeness index between water and forestwould provide very favorable situation forthe wildlife for their optimum survival.Moreover, the national park is marked bythe absence of prominent grassland In suchsituations, the preys are more likely to beattracted by the open forest and shrubs fortheir food since these would serve as themost favorable substitute for grassland.Therefore, high likeness indices for dense-moderate forest as well as dense-openforest would be desirable. In contrast,

likeness indices for forest-settlement andfor forest-cropland should be avoidedbecause settlements and croplands aresource of disturbance for wildlife.

Connectivity metrics describe the spatialconnectedness of a landscape degree oflinkage between the habitat patches. Thepatch based connectivity determined for boththe data sets 1990 and 2000 are 0.4026and 0.4951 respectively. The patch-basedconnectivity of 2000 data is greater than thatof the 1990 data. It indicates an increase inthe habitat quality in terms of connectivity.

Changes in Habitat Quality

Change pattern of the landscape eithersupports or inhibits survival of species. Thisemphasizes the importance of examiningpotential methods for analyzing landscapepattern. In the present study, we calculatedHabitat Quality Index (HQI) by treating eachLULC classes as habitat type and theresults are presented in Table 4.

The number of pixels under each habitattype was determined by rules and formuladescribed methodology section. Table 4shows a decrease in value of HQI duringthe study period. The values of HQI are0.4051 and 0.3762 for the year 1990 and

ECO-CHRONICLE246

2000 respectively. Thus, there is about7.14% decrease in the value HQI. Thissignificant decrease in habitat quality is dueto decease in the number optimal habitatpixel and increase in number of detrimentalpixel. The results obtained from habitatquality indexing which show a decrease inoverall habitat quality appeared to becontradictory with what we observed duringlandscape pattern characterization whichenvisaged some favorable change inspatial pattern of the landscape. There maybe several possible reasons for thiscontradiction. One possible reason is thedifficulty in finding the lower value of thesimple linear weighing method below zero.Hence, a negative weight cannot beassigned to the detrimental habitat ratherdetrimental pixels were eliminated byassigning them a zero weight. Anotherpossible is that in HQI calculation,adjacencies among the non-forest LULCclasses were not considered and theiradjacencies with forest classes werealways considered as unfavorable situationfor assessment of habitat suitability.


Natural resource planning andmanagement is becoming increasinglycomplex in the study site because ofescalating human activity in the in thelandscape. Pattern change, a crit icalcomponent of habitat analyses, resultingfrom landscape dynamics has directimplication to resource management andwildlife habitat assessment of the nationalpark. This paper provided an approach formonitoring the influence of landscapedynamics on the habitat quality in andaround the Betla National park. The resultsof the present study re-iterate theusefulness of remote sensing and GIS indetermining the quality of the habitat throughquantitative landscape analysis. The habitatsuitability of the landscape was found to bemoderate and the change in the habitatsuitability of the landscape is less than 5%,which may be considered to be negligible.Due to the effect of different driving forces,

the landscape has undergone a moderatechange in i ts spatial pattern andcomposition. However, the actual impact onwildlife quality of the landscape is little dueto restoration and conservationmeasurement taken by the forest authoritywhich nullified the detrimental effect oflandscape dynamics. Along with theattempted landscape analysis, otheranalysis such as terrain analysis andnearest neighbourhood along with morefield data for supporting landscape analysiscan make the result more reliable. Moreover,successful application of landscapeanalysis depends on the selection ofpattern metric that best relate the ecologicalprocess, metric sensitivity and their scaledependency for a particular study.

REFERENCES

Anderson, J.R., Hardy, E.E., Roach J.T. andWitmer, R.E., 1976. A Land Use and LandCover Classification System for Use withRemote Sensing Data, U. S. GeologicalSurvey Professional Paper 964. U.S. Govt.Printing Office, Washington, D. C.

Baker, W. L. and Y. Cai., 1992. The .rleprograms for multiscale analysis oflandscape structure using the GRASSgeographical information system,Landscape Ecology, 7, pp: 291-302.

Civco, D. L., Hurd, J. D., Hoffhine, E. andArnold, C. L., 2002. Quantifying anddescribing urbanizing landscape inNortheast United State, PhotogrammetricEngineering and Remote Sensing, 68 (10),pp: 1083–1090.

Frohn, R.C., 1997. Remote Sensing forLandscape Ecology: New Metric Indicatorsfor Monitoring, Modeling, and Assessmentof Ecosystems, Lewis Publishers, BocaRaton, FL.

Hargis, C. D., Bissonette, J. A. and David, J.L., 1997. Understanding measures oflandscape pattern. In W ildl i fe and

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landscape ecology: effects of pattern andscale (Ed. Bissonette, J. A). Springer-Verlag,New York, pp: 231–261.

Harris, L. D. and Sanderson, J., 2000.Landscape ecology; A top down approach,Lewis Publication, pp: 10-123.

O’ Neill, R.V., Krummel, J.R. and Gardner,R.H., 1988. Indices of landscape pattern,Landscape ecology, 1, pp: 153-162.

McGarigal, K. and Marks, B.J., 1995.FRAGSTATS: spatial pattern analysisprogram for quantifying landscapestructure. Gen. Tech. Report PNW-GTR-351,USDA Forest Service, Pacific NorthwestResearch Station, Portland.

Milde´n, M., Mu¨nzbergova´, Z., Herben, T.and Ehrle´n, J., 2006. Metapopulationdynamics of a perennial plant, Succisapratensis, in an agricultural landscape,Ecological Modeling. 199, pp: 464–475.

Reineking, B., Veste, M., Wissel, C. andHuth, A., 2006. Environmental variability andallocation trade-offs maintain speciesdiversity in a process-based model ofsucculent plant communities, EcologicalModeling, 199, pp: 486–504.

Riiters, K. H., Wickham, J. D., Vogelmann,J. E. and Jones, K. B., 2000. National landcover pattern data, Ecology, pp: 81, 604.

Schumaker, N. H., 1996. Using landscapeindices to predict habitat connectivity,Ecology, 77, pp: 1210–1225.

Shannon, C. and Weaver, W., 1949. Themathematical theory of communication.Univ. Illinois Press, Urbana.

Turner, M.G., 2005. Landscape ecology inNorth America: past, present, and future,Ecology, 86, pp: 1967–1974.

Turner, M. G., Gardner, R. H. and O’Neill, R.V., 2001. Landscape Ecology in Theory andPractice, Springer-Verlag, New York.

Walker, P.A., 1990. Modell ing wildli fedistributions using a geographicinformation system: kangaroos in relationto climate, Journal of Biogeography, 17, pp:279-289.

Zonneveld, I.I. and Forman, R.T.T., 1990.Changing Landscapes: An EcologicalPerspective, Springer-Verlag, New York, pp:286-288.

ECO-CHRONICLE248

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STATE OF MANGROVES IN VELLIKKEEL OF KANNUR Dt., KERALA

Sreeja, P. and K. M. Khaleel

P. G. Department of Botany and Research Centre, Sir Syed College, Taliparamba,Kannur District, Kerala.

ABSTRACT

The mangroves in three major sites (Vellikkeel - Chera, Padiyil Kadavu, Vellikkeel - Moolai) ofVellikkeel river was assessed and documented with respect to total area covered, diversity,population structure and threats to mangroves. The study was conducted in Nov.-Dec. 2008 usingplot quadrant method. Ten plots were non- randomly distributed and eight major and three associatedmangroves were found in the study area. The four dominant species were Kandelia candel(100%), Avicennia officinalis (80%), Sonneratia caseolaris (90%) and Excoecaria agallocha(80%). These species also had the highest important values at 87.47, 50.48, 21.94 and 43.10respectively. Considering the small size of the mangrove stand, the diversity was relatively highcompared to other areas in Kannur. However, continuous expansion of shrimp farms and developmentof various industries poses threat to the survival of these mangroves. Hence demarcation of thepresent mangrove area in Vellikkeel is needed.Key words: Mangrove, Vellikkeel.

INTRODUCTION

Mangroves represent a highly dynamic andfragile ecosystem. It occupies a largefraction of the tropical coastline, dominatingthe inter tidal zone of diverse environmentalsettings. The potential role of mangroveecosystem as sinks for anthropogeniccontaminants in tropical and sub tropicalareas has been widely recognized. Indiahas a total area of 4461 sq.km undermangroves. Mohanan, 1997 combinedremote sensing data and field observationsand reported that the extent of mangroveecosystem in Kerala is about 4,200 ha. Thehigher population density in the Kerala coasthas resulted tremendous pressure on thenatural ecosystem, which adversely affectedthe growth of mangroves. On surface surveythin mangrove patches were reported fromCochin estuary and some areas in NorthMalabar of Kerala. In Kannur, mangrovesare scattered in the area of Pappinisseri,Kunhimangalam, Thalasseri, Edakkad etc.(Naskar, K and Mandal, R 1999). Thesemangroves have attracted attention for its

floristic and faunal diversity. The Vellikkeelriver, a continuation of Kuttikkol river whichjoins with Payangady river at Dalil, supportsthree mangrove rich sites ie. Vellikkeel –Chera , Padiyil Kadavu and VellikkeelMoolai. Among these mangrove stand, 8hectares were with closed canopyrepresenting primary and secondary growthconditions dominated by true mangrovespecies. This paper reports the status ofmangroves in the wetlands of Vellikkeel riverin terms of total area covered, speciescomposition and community structure.

MATERIALS AND METHODS

This study was conducted in the Northernpart of Kerala. The geographical position isN- 12 0.217 and E - 75 21.012. The totalarea was found out by using GlobalPositioning System (GPS). The area can bedivided into three sites.

Site 1 Vellikkeel – CheraSite 2 Padiyil KadavuSite 3 Vellikkeel – Moolai


ISSN:0973-4155

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All mangrove species encountered wererecorded at species level using Tomlinson(1986). To assess the community structurein terms of density, frequency anddominance, a total of 10 plots (10x10 m²)were non- randomly established. In eachplot the species were identified, stems perspecies were counted and the girth of eachtree was measured at breast heightapproximately 1.3 m above the groundusing a measuring tape. The number ofindividuals per species was determined byactual counts. The important value index (IVI)and the Shannon Weaver species indexwere measured.

Site Specifications

The major adjacent components whichthe researcher thought to be the majorfactors in f luencing the existence ofmangroves in the local i t ies wereidentified. These include the residentialarea, weaver’s industr ia l soc ie t ies,agricultural field including shrimp farmsetc. Among them the growth of shrimpfarms seems to pose major threat tomangroves. At the same time the nonpractice of kaipad (salty wetland) ricecultivation contributes the over growth ofmangroves in these localities.

Table 1 (a)

Vellikkeel - Chera - Site 1 Padiyil Kadavu - Site 2No Species

F RF RD RDO IVI Pi logPi F RF RD RDO IVI Pi log Pi

1Acanthusilicifolius 50 11.3 9.8 0.013 21.11 -0.295 70 17.07 10.48 0.36 27.91 -0.1157

2Achrostichumaureum 60 13.6 16.46 0.005 30.1 -0.191 60 14.63 12.23 0.131 26.99 -0.137

3Aegicerascorniculatum 30 6.8 2.69 0.035 9.52 -0.061 10 2.43 3.49 0.73 6.65 -0.0819

4Avicenniaofficinalis 80 18.18 18.26 0.507 36.94 -0.149 90 21.95 27.97 0.56 50.48 -0.3579

5Excoecariaagallocha 80 18.18 24.8 0.123 43.1 -0.31 40 9.75 4.89 2.94 17.58 -0.127

6Kandeliacandel 90 20.45 29.3 0.856 50.6 -0.379 --- --- --- --- --- ---

7Rhizophoramucronata --- --- --- --- --- --- 90 21.95 22.02 88.3 132.3 -0.2686

8SonneratiaCaseolaris --- --- --- --- --- --- 10 2.43 7.69 1.39 11.51 -0.2129

9Pandanustectorius 20 4.5 2.09 0.066 6.65 -

0.0209 --- --- --- --- --- ---

10Clerodendroninerme --- --- --- --- --- --- 40 9.75 11.18 0.42 21.35 -0.1242

11Anonapalustris 30 6.8 2.39 0.219 9.48 -0.053 --- --- --- --- --- ---

H’ = - Pi log Pi = 1.4589 H’ = - Pi log Pi = 1.4252

F – Frequency, RF – Relative Frequency, RD – Relative Density, RDO – Relative Dominance,IVI – Important Value Index, H’ – Shannon Wiener’s Diversity Index.

251ECO-CHRONICLE

Vellikkeel - Moolai - Site 3No Species

F RF RD RDO IVI Pi log Pi

1Acanthusilicifolius 70 13.46 6.44 0.024 19.92 -0.1705

2Achrostichumaureum 60 11.53 4.63 0.007 16.14 -0.1134

3Aegicerascorniculatum --- --- --- --- --- ---

4Avicenniaofficinalis 90 17.3 9.79 0.56 27.66 -0.291

5Excoecariaagallocha 70 13.46 1.37 0.17 15.01 -0.029

6Kandeliacandel 100 19.23 68.2 0.041 87.47 -1.147

7Rhizophoramucronata 40 7.69 7.69 0.116 15.49 -0.2129

8SonneratiaCaseolaris 90 17.3 4.517 0.072 21.94 -0.1107

9Pandanustectorius --- --- --- --- --- ---

10Clerodendroninerme --- --- --- --- --- ---

11Anonapalustris --- --- --- --- --- ---

Table 1 (b)

H’ = - Pi log Pi = 2.0745


The total mangrove area is about 15hectares. The area composed eight truemangrove species and three associates.This includes Acanthus i l icifol ius(Acanthaceae), Achrostichum aureum(Pteridaceae), Aegiceras corniculatum(Myrsinaceae) , Avicennia off icinalis(Verbenaceae), Excoecaria agallocha(Euphorbiaciae) , Kandelia candel(Rhizophoraceae), Rhizophora mucronata(Rhizophoraceae), Sonneratia caseolaris(Sonneratiaceae) Pandanus tectorius(Pandanaceae), Clerodendron inerme

(Verbenaceae) and Anonapalustris (Anonaceae ).

The number of major speciesfound in Vellikkeel is lowercompared to the recordednumber of species of 14 atKunhimangalam ( CED 2005 ).Mangroves in the study areashowed a typical zonationbased on the entry of salt water.Species found in Padiyil Kadavu(seaward zone) were mainlyRhizophora mucronata andAvicennia officinalis. Centralpart of Vellikkeel supportsKandelia candel andExcoecaria agallocha.Vellikkeel Moolai zone (landward zone) containsKandelia candel andSonneratia caseolaris. Amongall the mangrove species,Rhizophora mucronata has thehighest basal area followed byAvicennia officinalis. Kandeliacandel has the highest stemdensity followed by Avicenniaofficinalis. The other species ofmangroves were ratherinconspicuous in terms ofbasal area and stem number.In the site-1 Kandelia candelhas the highest frequencyfollowed by Excoecariaagallocha and Avicenniaofficinalis. In site-2 Rhizophora

mucronata and Avicennia off icinalisoccurred in nine plots. In site-3 Kandeliacandel occurred in all the plots followed bySonneratia caseolaris and Acanthusilicifolius. The relative dominance of all thespecies in the study area ranged between0.006% to 88.3% . The relative density ofthe species ranged from 1.370-68.2 in allthe study sites. The dominance ofRhizophora mucronata in the second sitemay be due to the muddy substrate thatfavours its growth (Hogarth,1999). TheAegiceras corniculatum accounts for thelowest IVI value in the first and second sites.The Diversity Index of mangroves in

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Vellikkeel using the Shannon index was1.486 , 1.4201 and 2.075 respectively forthe three sites. The diversity index signifiesthat the area is rich in species diversity(Table 1a & b).

The mangroves in Vellikkeel are subjectedto human disturbances. In fact, part of thearea had been already cleared and most ofthem destructed for construction purposes.A portion of the mangrove forest had beenconverted into shrimp farming land.Considering the small size of the mangrovestand, the diversity was relatively highcompared to other areas in Kannur.However, continuous expansion of shrimpfarms and development of variousindustries poses threat to the survival ofthese mangroves. Improper waste disposal(domestic, slaughter and industrial) anddevelopmental activities may severely affectthe stands of most of the mangrove species.Hence demarcation of the presentmangrove area in Vellikkeel is needed forits protection and conservation.

CONCLUSION

Based on the study, it was concluded thatthe area supports a rich diversity ofmangroves representing eight truemangrove species. Improper wastedisposal (domestic, slaughter andindustrial) and developmental activities mayseverely affect the stands of most of themangrove species. On the other hand thereis a rapid increase in the mangroves mainlyin the third site within the last ten years due

to the non practice of “kaipad” cultivation.Thus demarcation of the present mangrovearea in Vell ikkeel is needed for i tsconservation.

ACKNOWLEDGEMENT

The authors are thankful to P G Dept. ofBotany and Research Centre , Sir SyedCollege Taliparamba and KSCSTE for thelaboratory and financial supportrespectively.

REFERENCES

Hogarth, P. J., 1999. The Biology OfMangroves: Oxford university Press Inc.New York.

Tomlinson, P. B., 1986. The Botany ofMangroves :Cambridge University Press,413 pp.

C E D., 2003. Survey and Inventory ofwetlands of Kerala for conservation andsustainable management of Resources -Vol – 5.

Naskar, K and Mandal, R., 1999. Ecologyand Biodiversity of Indian Mangrove part Iand II Daya Publishing House,New Delhipp 131 – 167.

Mohanan, C. N., 1997. Mangroves ofKerala. In: Balachandran Thampi, et al.(eds.) Natural Resources of Kerala. WWF,Trivandrum.

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ISSN:0973-4155

AN ERGONOMIC ANALYSIS OF HAND TOOLS/FARM EQUIPMENTS INHOMESTEADS OF KOLLAM DISTRICT, KERALA

Bini Sam

Kerala Agricultural University, Farming Systems Research Station, Sadanandapuram,Kottarakkara, Kerala

ABSTRACT

Farm mechanisation along with increased application of other inputs such as seeds, fertilizers,pesticides, insecticides etc. has enhanced the productivity and production on farms. But on theother hand it has also lead to increased discomfort, drudgery and injuries to the operators whilecarrying out different agricultural activities due to the inadvertent neglect of ergonomic principles.In order to identify the ergonomic problems encountered by the users during the operation ofvarious agricultural tools/implements, a survey was conducted in the homesteads of Kollam district,Kerala, using prestructured questionnaire. The study shows that high rate of work, awkward workposture and design deficiencies of the hand tools result in many musculo-skeletal strain and injurieswhile carrying out different farming activities.

Key words: farm mechanisation, drudgery, ergonomic principles

INTRODUCTION

Homestead farms of Kerala are peculiar inthat they are very small farming unitsmanaged independently by differentfarmers. In spite of the progress in farmmechanization and irrespective of farmercategory (small, medium and large), handtools are widely used in these farms forvarious field operations like sowing, hoeing,weeding etc. Various state agriculturaluniversities and other research instituteshad developed quite a large number of handtools and agricultural equipments. Most ofthe designers of agricultural equipmentshad concentrated on improving theefficiency and durability, but none of themgave due importance to the operatorscomfort and treated operator as an otherpart of a man-machine system. No reportsare available regarding the ergonomicproblems in agriculture in the homesteadsof Kerala. Hence this study is designed toassess the ergonomic problems faced bythe users during the operation of hand tools/

farm equipments in specific situationsprevailing in the homesteads of Kollamdistrict, Kerala.


For the study, stratified two stage randomsampling procedure was followed. Kollamdistrict consists of 70 panchayats, out ofwhich 10% of panchayats were selectedrandomly. From the homestead farmers ofthe selected panchayats, 10 farmers wereselected at random. The data was collectedfrom the farmers with pretested schedules.The data collected was analysed by usingappropriate statistical techniques.

A comprehensive survey questionnaire wasthus prepared and information wasgathered from the farmers. Thequestionnaire was designed to obtain datarelated to total land holding pattern,cropping pattern, hand tools/ farmimplements currently in use, drudgeryinvolved in the operation of hand tools/ farm

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implements, type and cause of accidentsin the operation of hand tools/ farmimplements and nature of injury/illness andthe body parts affected while using theequipments.


Spade was the tool used by majority offarmers. It was mainly used for seedbedpreparation, which involves selective fieldtasks such as breaking up a hard surface,breaking down the ploughed surface andinter-row soil preparation to grow crops. Asickle is the most common weeding/harvesting tool used by the farmers and itconsists of a curved blade attached to awooden handle, however, with a variety ofdesigns. The sickle ergonomics, bladegeometry, blade serration and material,handle shape and size and mechanics ofoperation have their effects on workperformance.

About 46% of the total farmers were of theopinion that the hand tool requires a lot offorce for its operation (Table 1). Farmers(25%) expressed that “too much” force wasrequired for some operation. According toDe and Sen,1986, the severity of seedbedpreparing activities, like ploughing, hoeing,bund trimming vary from moderate toextremely heavy, which are primarily beendone by the men - folks.

Majority of farmers opined that it is difficultto operate the hand tools, especially handhoe and spade, continuously for more than2 hours. Some farmers felt spade as ahazardous tool causing leg injury. Around41% of farmers responded that theoperation of the hand tools involves a lot of

drudgery followed by “too much” drudgery(33%) and some drudgery (24%). Nag et.al., (1980) and Nag and Chatterjee (1981)suggested that the work levels for 8 hourlyactivities for men and women should notexceed 35% and 28% of one’s aerobiccapacity, respectively. To avoidaccumulation of fatigue, it is obvious toformulate work-rest sequence inaccordance with the types of physicalactivities performed.

The pains expressed by the farmers indifferent parts of the body while operatingthe hand tools are shown in Tables 2 & 3.

About 79% of farmers suffered from backpain while 50% of farmers suffered fromshoulder pain followed by pain in hands(43%), waist (36%) and leg (14%) duringdigging operation. Similarly for weedingoperation, around 75% of farmers felt backpain followed by waist pain (50%) and painin shoulders and hands (42 % each). Theback pain was mainly due to the stooping /bending posture adopted by the farmersduring the operation of hand tools. In drylands, workers remove weeds by sitting onthe ground with one or two legs flexed atknee, whereas in the watered land, theworkers stoop to remove weeds. Each wayof doing the weeding tasks exerts posturalstress. Since the hand hoe and spadedesigns have been evolved through localpractices, there are lots of variations intechnical specifications.

Wounds / abrasion / corns etc., were othercommon health hazards faced by thefarmers. Most of the farmers reported thathandle of the tools was not satisfactory.Farmers also faced frequent breakdown ofparts like handle and edge of tools duringwork. Since operation of hand tools involvesa lot of drudgery, modification is necessaryfrom ergonomic point of view such as angleof blade with handle, length of handle andtotal weight of the tool.

Drudgery / health hazard involved in sprayingoperation is shown in Table 4. The mainfunction of spraying is to atomize the spray

Sl.No.

Classification offorce

Percentresponse

1 Too much 252 A lot 463 Some 314 No 0

Table 1. Force required in the operation oftool / equipment

255ECO-CHRONICLE

fluid into small droplets and eject the sameto give uniform deposit on the target. Thesprayer equipments were manuallyoperated, like the hand compressionsprayer, lever operated knapsack sprayerand rocker sprayer. Around 60 % of farmersexpressed dizziness and 50 % of farmersfelt skin irritation while applying pesticides,fol lowed by eye irri tation (33%) andbitterness in mouth (16 %). The sprayeroperators experience fatigue mainly due tocarrying of the sprayer load as well ascontinuous lever operation. The pesticideapplicators, mixers and loaders are at riskof exposure to toxic chemicals. It is notuncommon that the farmers broadcastpesticides or prepare pesticide solution with

bare hands. Improper handling ofpesticides, spraying without wearingpersonal protective equipments etc. led tomany sprayer related accidents. The healthand safety concerns demand institutionalmeasures for comprehensive training onpesticide safety, dress code, emergencyassistance in case of exposure etc. Theimplementation feasibility of precautionarymeasures requires to be examined, sinceit is a difficult behaviour to enforce in thetropical areas where protective wears addto the heat stress of the wearer ( Nag andNag, 2004).

The survey reported that 30 % of all injurieswere caused by ‘striking by’ or ‘strikingagainst’ the tool followed by cut (25 %) dueto the improper handling of tools. Thepercentage distribution of the nature of injuryindicates that cuts followed by lacerations,sprains and strains are the most frequentinjuries in using hand tools. Study alsoshowed that fingers and limbs were themost injured parts of the body. The high rateof work, awkward work posture and designdeficiencies of the hand tools result incumulative musculo-skeletal strain andinjuries. The sickle related injuries mostlyoccurred while harvesting hard-stem crops.


On the basis of survey, it may be concludedthat there exists a lot of drudgery / healthrisk in various agricultural operations. Thereis much scope for ergonomic interventionfor reducing drudgery in different agriculturaloperations by making design modificationsin various equipment/machines and handtools. The physical strain and fatigue mightresult in accidents and injuries, andtherefore, the work levels that may bemaintained daily on a regular basis shouldbe optimized. It is also noticed that most ofthe body pains caused in performing theagricultural operations was due to posturaldiscomfort as in digging with spade/handhoe, manual weeding etc. Improperhandling of pesticides, spraying withoutwearing personal protective equipment, etc.

Sl.No.

Parts of body Per centexpressed pain

1 Back 792 Waist 363 Shoulder 504 Hands 435 Legs 14

Table 2. Pain in different parts of bodydue to digging operation

Sl.No.

Parts ofbody

Per cent expressedpain

1 Back 752 Waist 503 Shoulder 424 Hands 42

Table 3. Pain in different parts of body dueto weeding operation

Sl.No.

Parts of body Per cent response

1 Skin irritation 502 Eye irritation 333 Dizziness 804 Bitterness in

mouth16

Table 4. Drudgery / health hazard involvedin spraying operation

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led to many sprayer related accidents. Thehealth and safety concerns demandinstitutional measures for comprehensivetraining on pesticide safety, dress code,emergency assistance in case of exposureetc. Periodic trainings need to be conductedin proper and safe operation of sprayersand equipments. Personnel protectiveequipment like hand gloves and masksshould be used by the operators to avoidhand injuries and dust hazard. Extensionleaflets/publicity materials for proper andsafe use of various machines need to beprepared and circulated on a wider scale.

REFERENCES

De, A. and Sen, R.N., 1986. Ergonomic

evaluation of ploughing process of paddycultivation in india, J. Human Ergology, 15,pp: 103 - 112.

Nag, P.K. and Chatterjee, S.K., 1981.Physiological reactions of female workersin Indian agricultural work, Human factors,23, pp: 607 - 614.

Nag and Nag, P.K., 2004. Drudgery,accidents and injuries in Agriculture,Industrial Health, 42(2), pp:149-162.

Nag, P.K., Sebastian, N.C., Malvankar,M.G.,1980. Effective heatload on agriculturalworkers during summer season, Ind J MedRes, 72, pp: 408–15.

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ISSN:0973-4155

BIOCHEMICAL CHANGES IN CERTAIN PLANTS SPECIES UNDERAUTOMOBILE EXHAUST POLLUTION

Sarala Thambavani, D.1 and Syed Ali Fatima Lathif 2

1 Department of Chemistry, Sri Meenakshi Government College,Madurai - 625002, Tamilnadu.

2 Vinayaka Mission University, Salem, Tamilnadu.

ABSTRACT

Biomonitoring experiments were conducted to evaluate air pollution due to automobile exhaust onthe vegetation along the roads of Madurai city, TamilNadu. The plant species selected for thepresent study included, Thespesia sp., Morinda tinctoria, Tectona grandis, Polyalthia longifoliaand Ficus religiosa. Estimation of Chlorophyll, Total soluble sugar, Protein and Aminoacids werecarried out to study the impact of air pollutants. The present study on these plants revealed thatvegetation on roadside was much affected by vehicular emission. Significant variation in thesecontents was observed. It is inferred that plants can be used as indicators of urban air pollution andthere is urgent need to protect these from air pollution.

Key words: Biomonitoring, Bioindicators, Chlorophyll content, Total soluble sugar, Protein andAminoacid.

INTRODUCTION

The increase in world population isgenerating industrialization, urbanizationand of course modernization. People saythat these are the criterias for thedevelopment of any nation but on thecontrary, these are also the contributors forthe disastrous effects causing to nature andenvironment. In developed countries, thesituation is somewhat out of control but inthe developing countries the situation canbe handled as there is still time for them.As industrialization and urbanization isincreasing, the percentage of smoke, dustand ash are increasing in the ambient airand the natural environment is deteriorating.

With the continuous multifold increase invehicular population, emissions are givingrise to an alarming rate of air pollution andconsequently deterioration of air quality. Themain primary air pollutants emitted frommotor vehicles include Oxides of nitrogen(NOx), Carbon monoxide (CO), Respirablesuspended particulate matter (RSPM/PM10),

Sulphur dioxide (SO2) and Volatile OrganicCompounds (VOC).These pollutants havevarying degrees of harmful effect.

Vegetation plays the role of major sink ofatmospheric gases and dust containing afair amount of highly toxic heavy metalparticles. Air pollution may affect planttissues either directly or indirectly, but thetolerant plants possess strong defensemechanisms which enable them to survivein critical conditions. Plants are to suffer alot from the automobile exhaust pollutionbecause they cannot move from the sourceof pollution. For this reason it is likely thatplants exposed to such pollution show manyabnormalities in their general appearancewhich are termed as “visible injury” inliterature. Actually these visible injuries onplants to a greater extend reflect thephysiological changes which occur due tothe impact of pollutants. Thesephysiological changes may be regarded as“hidden injury”. These plants are able toavoid these senescing symptoms and can

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live a normal way. In this paper, an attempthas been made to study the effect ofvehicular emission on the biochemicalparameters of some plants species.


Madurai has an area of 52 square km (nowextended up to 130 square km) and islocated at 9.93° North 78.12° East. Theaverage elevation is 101 meters above MeanSea Level. The climate is dry and hot, withrains during October to December.Temperature during the summer reachesto maximum of 40.00C and minimum of26.30C, where as during winter thetemperature reaches to a maximum of29.60C and a minimum of 18.00C.Theaverage annual rainfall is 85cm (850mm).

Selection of sampling station

Madurai consists of a large mixed usearea, having high traffic column and also avery fast growing center for tourism. The city

has also emerged as an important centerfor textiles and engineering industries. Thesampling stations were selected keepingin view the important zone and the nature ofactivity. A total of two sampling stations,consisting of residential and traff icintersections were chosen for the presentstudy. The description of sampling stationis given in Table 1.

Two sampling stations have been selectedto study the impact of air pollution onvegetation, which include high traffic volumeand residential area that is mixed useareas. South gate is purely a residentialarea, having less traffic. Periyar bus standis commercial area with heavy traffic load.

Experimental Material

The field plants, which were selected forthe study were:1. Thespesia sp. 2. Morinda Tinctoria3. Tectona grandis 4. Polyalthia longifoliaand 5. Ficus religiosa

Sampling station Nature of activity

South gate area of Madurai Residential area

Periyar bus stand of Madurai Major traffic of heavy vehicles. Most busy trafficjunction. (Commercial cum traffic area)

Table 1 Description of sampling station in Madurai city

Sampling stations in Madurai city

259ECO-CHRONICLEPreliminary survey of the Madurai Road sideshowed that these plants are in highestfrequency and density. Hence these wereselected for the present study.

Collection of leaf samples

Sampling of leaf material is an importantstep in any air pollution studies. In thepresent study, leaf samples from the twosampling stations such as South gate andPeriyar bus stand were collected both at 12Noon and at 6.00 PM. The samplescollected both in afternoon and evening isexpected to express phantom variation inphysiological and biochemical parameters.This might not be a true expression by theplant against air pollution. Because themetabolic activity would be more (up tonoon) when the plants exposed to light aftera long dark period. The leaf samples werecollected accordingly by giving six hoursinterval between two samples, so that thecomparison on effect of pollution on leafsamples would be reliable. 500 grams ofleaf samples were collected from the twosampling sites and at two different times(12 Noon and 6.00 PM) and were thensubjected to analysis.

Determination of different parameters:Chlorophyll content in the leaf samples were

analysed by following the methods of Arnon1949.Total soluble sugar by phenolsulphuric acid method Dubois et al.,1951.Protein by Lowry et al., (1951)and freeamino acids by Moore and Stein 1948.Results were recorded.


In the present study, in order to obtain theinformation on the type, intensity and thecause of vegetation damage, five differentplant species and two locations werestudied at two different times.

Estimation of chlorophyll, total solublesugar, protein and aminoacids were carriedout for monitoring automobile exhaustpollution impact on plants. The five selectedspecies Thespesia sp, M.tinctoria, Tectonagrandis, P.longifolia and F.religiosa werecollected at random from the edges of bothsites of roads of Periyar bus stand andSouth gate area of Madurai city, TamilNadu.The roads of Periyar bus stand are having ahigh traffic volume of at least 50 to 70motorized vehicles per minute.The samespecies are collected from south gate areaof Madurai, a residential area.

The leaves collected from the polluted areaand the residential area were put in for

Species Station / Sites Chlorophyllcontent(mg/g)

TotalSolubleSugar(mg/g)

Protein(mg/g)

Amino Acid(mg/g)

Control area 0.98 2.27 0.95 181.5Thespesia sp.

Polluted site 0.36 0.72 0.87 194.7Control area 0.33 2.11 1.63 136.95M.tinctoriaPolluted site 0.32 1.64 1.33 107.25Control area 0.25 2.08 0.84 136.95

T. grandisPolluted site 0.50 2.58 1.46 186.45Control area 0.35 1.54 0.75 75.9P. longifoliaPolluted site 0.52 2.19 1.38 174.9Control area 0.21 1.75 0.78 107.4

F. religiosa Polluted site 0.50 2.19 0.99 141.9

Table 2. Biochemical Indicators of different species at different bioindicator stations

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estimation of certain vital parameters likechlorophyll (mg/g), total soluble sugar (mg/g), protein (mg/g) and aminoacids (mg/g).These parameters showed significantvariation under polluted environment,indicating the potentiality of plants to be usedas bioindicators of air pollution.


In the present study, air pollutant from theautomobile exhaust emission shows widespread damage to the plants. In order toobtain the information on the type, intensityand the cause of vegetation damage in fivedifferent plant species are studied at twodifferent time and at the polluted and theunpolluted sites.The chlorophyll content, the total solublesugar, protein and aminoacids are used asparameters for monitoring automobileexhaust pollution impact on biochemicalchanges of plants. The five selected species

Thespesia, M.tinctoria, Tectona Grandis,P.longifolia and F.religiosa are collected atrandom from the edges of both sites ofroads of Periyar bus stand and South gatearea of Madurai city, TamilNadu. The roadsof Periyar bus stand are having a high trafficvolume of atleast 50 to 70 motorizedvehicles per minute. The same species arecollected from south gate area of Madurai,a residential area.

The leaves collected from the polluted areathat is high traffic volume area and aresidential area are put in for estimation ofcertain vital biochemical parameters likechlorophyll content(mg/g), the total solublesugar(mg/g), protein (mg/g) andaminoacids (mg/g). These biochemicalparameters shows significant variationunder polluted environment. Hence, theseplant species can be graded asbioindicators of air pollution.

-150

-100

-50

0

50

100

Thespesia MorindaTinctori

TectonaGrandis

PolyalthiaLongifolia

Ficus Religiosa

Perc

ent v

ariat

ion

Chlorophyll content

-50

-40

-30

-20

-10

0

10

20

30


TectonaGrandis


FicusReligiosaPe

rcen

t var

iation

Total Soluble Sugar

Variation in Chlorophyll content (Figure 1) and Total Soluble Sugar content (Figure 2)of five different plant species from different Bioindicator stations

Figure 2

Figure 1

261ECO-CHRONICLE

The Chlorophyll content of plants studiedexhibited significant variation with respectto stations. Thespesia sp. exhibited 63.27%reduction in chlorophyll content at thepolluted site. M. tinctoria showed 3.03%reduction at the polluted site compared tothe residential area. Thespesia sp. showedsignif icant reduction compared to M.tinctoria, T. grandis, P. longifolia and F.religiosa exhibited an increasing trend ofchlorophyll at the polluted site. Maximumgain of 138.10% of chlorophyll was shownby F. religiosa followed by Tectona grandis(100%) and P. longifolia (48.57%) at thepolluted site. A considerable loss in totalchlorophyll in the leaves of plants such asThespesia sp. and M. tinctoria exposed tothe automobile exhaust pollution supportsthe argument that the chlorophyll is theprimary site of attack by air pollutants suchas oxides of carbon, oxides of nitrogen,oxides of sulphur and suspendedparticulate matter. The air pollutants maketheir entry into the tissues through the

stomata and cause partial denaturation ofthe chloroplast and decreases pigmentcontents in the leaf cells. But F. religiosia, P.longifolia and Tectona grandis showed anenhancement in the chlorophyll content atthe polluted site. This may be due to theleaf arrangement, type of leaf and the totalleaf area of a plant which promote maximumutilization of light available. The leaves ofthese plants oriented at acute angles to thegeneral direction of illumination are foundtowards the crown. Apart from the smallamount of light which they reflect andtransmit gaps between them allow a gooddeal of light to pass directly to leaves below.Underneath are shade-leaves oriented atright angles to the general direction ofillumination and arranged in a fashion,which ensures maximum interception of thelight.

The total soluble sugar content in the leavesof Thespesia at the unpolluted and thepolluted sites are 1.72 mg/g and 2.27 mg/g

-100

-80

-60

-40

-20

0

20

40


TectonaGrandis


FicusReligiosa

Perce

nt va

riatio

n

Protein

-140-120-100-80-60-40-20

02040


TectonaGrandis


FicusReligiosa

Perce

nt va

riatio

n

Aminoacid

Figure 3

Figure 4

Variation in Protein content (Figure 3) and Amino Acid content (Figure 4) of five differentplant species from different Bioindicator stations

ECO-CHRONICLE262

respectively. The maximum reduction (24.23%) in total soluble sugar is observed inThespesia at the polluted site. M. tinctoriashowed 22.27 % loss at the polluted site.Tectona grandis, P. longifolia and F. religiosaexhibited increasing trend of total solublesugar at the polluted site. P. longifolia (42.21%) showed the maximum followed by F.religiosa (25.14%) and Tectona grandis(24.03 %). Thespesia sp. and M. tinctoriashowed significant loss of total solublesugar and also reduction in chlorophyllcontent for these two species at the pollutedsite. Total soluble sugar is an importantsource of energy for all living organisms.Plants manufacture this organic substanceduring photosynthesis and breakdownduring respiration. The concentration ofsoluble sugars is indicative of thephysiological activity of a plant and itdetermines the sensitivity of plants to airpollution. Reduction in soluble sugarcontent in polluted sites can be attributed toincreased respiration and decreasedcarbon dioxide fixation due to chlorophylldeterioration. Davison and Barnes (1986)mentioned that pollutants like oxides ofsulphur (SO2), oxides of nitrogen (NO2) andhydrogen sulphide under hardeningconditions can cause more depletion ofsoluble sugars in the leaves of plants grownin polluted area. The reaction of sulfite withaldehydes and ketones of carbohydratescan also cause reduction in carbohydratecontent.

Protein content of Thespesia showed 8.42%reduction at the polluted site. The proteincontent of M. tinctoria at the polluted and theunpolluted sites were 1.33 mg/g and 1.63mg/g respectively. Maximum reduction(18.41 %) in protein content is exhibited atthe polluted site by M. tinctoria followed byThespesia. Increase in protein content wasnoticed in Tectona grandis, P. longifolia andF. religiosa. Maximum increase in proteincontent is exhibited by P. longifolia (84%)followed by Tectona grandis (73.81%) andF. religiosa (26.92%). Reduction in protein

content of Thespesia and M. tinctoria at thepolluted site might be due to the enhancedrate of protein denaturation which is alsosupported by the findings of Prasad andInamdar (1990). Constantinidou andKozlowski(1979) found enhanced proteindenaturation and breakdown of existingprotein to aminoacid as the main causes ofreduction in protein content. Thespesia andM. tinctoria showed significant reduction inchlorophyll content. It implies that the rateof photosynthesis of these two plants is inthe decreasing trend at the polluted site.The decreasing trend of photosynthesis isdue to the diminished light and the darkreactions in the leaves. The rate ofphotosynthesis is proportional to theformation of total soluble sugar and plantprotein. The loss of chlorophyll at thepolluted site decreased the total solublesugar and protein content of these twoplants but Tectona grandis, P. longifolia andF. religiosa have the increase of chlorophyllcontent at the polluted site which enhancedtotal soluble sugar and protein content.

It is noted that the free aminoacid exhibitedan increasing trend at the polluted site forThespesia sp., Tectona grandis, P. longifoliaand F. religiosa. M. tinctoria showed areduction in aminoacid content at thepolluted site. P. longifolia showed themaximum increase (130.43%) ofaminoacid at the polluted site followed byTectona grandis (36.15 %) and F. religiosa(32.12%) and Thespesia sp. (7.27%). Allthe four species showed increased freeaminoacids at the polluted site but it variedwith the air pollution load. Plants frompolluted site (Periyar bus stand) exhibitedmaximum increase of free aminoacids ascompared to southgate area. More freeaminoacids at polluted site (Periyar busstand) may be due to more nitrate reductaseactivity or may also be due to more proteindenaturation at the polluted site. Nitratereductase is a metalloflavoprotein inducibleenzyme which catalyses the reduction ofnitrate to nitrite. It acts as a rate limiting step

263ECO-CHRONICLE

and regulatory enzyme in the pathway NO3NO2NH4aminoacids and its activityoften controls the overall assimilation rateof nitrate. There are two distinct pools fornitrate in plant tissues that is storage andmetabolic pools. Only nitrate of themetabolic pool functions as a substrate forNR and contributes to organic nitrogen. Inthe present investigation air pollution loaddependent increase in NR activity may bedue to the more availability of nitrate in themetabolic pool of the plants at more pollutedsite. The source of nitrate may be the oxidesof nitrogen (NOX), as a pollutant in theatmosphere. Zeevaart (1974) foundinduction of nitrate reductase activity inplants by atmospheric NO2.

CONCLUSION

Thespesia sp. and M. tinctoria showedsignificant reduction in chlorophyll, totalsoluble sugar and protein content at thepolluted site. Thespesia sp. showed anincreasing trend and M. tinctoria showeddecreasing trend of free aminoacids at thepolluted site. Tectona grandis, P. longifoliaand F. religiosa showed the maximumenhancement of chlorophyll content, totalsoluble sugar, protein and aminoacids atthe polluted site. Thespesia sp. and M.tinctoria experienced the air pollution loadand found to be more sensitive towards theautomobile pollution. Tectona grandis, P.longifolia and F. religiosa showed resistancetowards the air pollution load. Thephysiological disturbances due to airpollution change the biochemical status ofthe plants. Thespesia is a shrub and theheight of M. tinctoria is only 5 to 10 meters.The impact of automobile emission isexperienced more by these species. Hence,the photosynthetic activity of Thespesia sp.and M. tinctoria are in the decreasing trendat the polluted site. Thus these two speciesare sensitive towards automobile pollution.The height of Tectona grandis, P. longifoliaand F. Religiosa is ranging from 25 to 30meters. These three species are not

affected much by air pollution. Hence, thephotosynthetic activity of Tectona grandis,P. longifolia and F. religiosa are in theincreasing trend at the polluted site. Dataon ambient pollutant concentrations do notallow direct conclusions to be drawn onpotential impacts on plants and theenvironment. Evidence of effects can onlybe provided by using plants itself asmonitors. These types of plant bioindicatorsintegrate the effects of all environmentalfactors including interactions with otherpollutants or climatic conditions. Thispermits the risk of complex pollutantmixtures and chronic effects occurring evenbelow threshold values to be assessed.Therefore use of plants, as indicators isinexpensive and easy technique. Merely byanalysing the present parameters, an earlydiagnosis of the extent of pollution can bedone in the absence of visible injury.

REFERENCES

Arnon, D. I., 1949. Enzymes in isolatedchloroplast poly phenol oxidase in Betavulgaris, Plant physiology 24: pp:1-15.

Constantinidou, H. A. and Kozlowski, T. T.,1979. Effect of SO 2 and O3 on Ulmusamericana seedling 1. visible injury andgrowth 2. carbohydrate,proteins and lipids.Can. J. Bot. 57, pp: 107-184.

Davison, A. W. and Barnes, J.D., 1986.Effects of winter stress on pollutantresponses In: How are the effects of airpollutants on agricultural crops influencedby the interaction with other limiting factorsCEC Brussels. pp: 16-32.

Dubios, Gilles, M.K., Hamiltion, J.K., Rebers,P. A. and Smith, F., 1951. A colorimetircmethod for the determination of sugars.Nature: pp:167.

Jayaraman, J., laboratory manual inbiochemistry. pp: 107-110.

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Palanivel, P P., analytical biochemistry andseparation Techniques. pp:55-57.

Prasad, M.S.V. and Inamdar, J A., 1990.Effect of cement kiln dust pollution on black

gram(Vigna mungo) Proc. Indian Acad. Sci.(Plant sci.) 100(6), pp:435-443.

Zeevaart, A.J., 1974. Induction of nitratereductase by NO2 Acta. Bot. Neerl 23,pp:345-346.

265ECO-CHRONICLE

A STUDY ON SOME INDIGENOUS PLANTS WITH ANTI DIABETICPOTENTIAL OF NILAMEL GRAMA PANCHAYATH IN KOLLAM DISTRICT,

KERALA STATE

Venugopal, S. and Mahesh Kumar, M. R.P.G. Department of Botany, N.S.S.College, Pandalam, Pathanamthitta District, Kerala.

ABSTRACT

The present paper is a study of some important locally available plants with antidiabetic potential ofNilamel grama panchayath in Kollam District, Kerala State. The result of field survey revealed that25 dominant plants belonging to 21 families have antidiabetic potential which are most used as folkremedy.Keywords: Diabetis, indigenous plants

INTRODUCTION

The plants are being used from ancienttimes to maintain health, to treat diseasesand regain the healthy state of mind andbody. The herbal treatments are generallyharmless, beneficial and have curativeeffects. Besides, it offers conventionaltreatments, giving safe and naturalremedies for various ailments. The WHOrecognizes that traditional medicinal plantshave curing properties of many diseasesand there fore research should be steppedup. In recent years many efforts wererecorded. The ethno medicinal uses ofplants from various countries have receivedthe attention of scientists (Brahman andSaxena, 1989, Jain, 1981and Karthikeyani,2003).

Diabetis is a heterogeneous metabolicdisorder, characterized by alteredcarbohydrate, fat and protein metabolism(Devlin, 1997). According to thepharmaceutical research; natural productsare potential source of many drugdevelopments (Marles and Farnsworth,1995). According to common people, manylocal plants have anti diabetic potentialities.So we made an attempt to investigate mostcommon and locally available plants withanti diabetic potential.


The study involved extensive field survey formedicinal plants and interviews. Data werecollected from native informants who wereNattu vaidhyas (Aurvedic medicalpractitioners), sidhas and common people,who have a say regarding therapeutic valueof plants. Data were collected almost fromevery ward of Nilamel grama panchayath,Chadayamangalam block in Kollam district.This paper describes only most importantlocally available plants which are usedmedicinally.

RESULTAND DISCUSSION

In the present study 25 dominant antidiabetic plants most useful as folk remedywas described. These plants belonging to21 families.

The information about these plantscollected from local community wasconfirmed by referring the important workspertaining to Indian medicinal plants(Warrier, et al, 1996), Medicinal plants (Jain, 1968) and Indigenous drugs of India (Dey,1973; Singh et al., 2002; Pandey and Khan,2005). The purpose of the present paper isnot to prescribe remedies for diabetics but


ISSN:0973-4155

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Sl.No.

Botanical name Local name(Malayalam)

Family Plant partused

Habit

1 Achyranthes asperaL.

Kadaladi Amaranthaceae Whole plant An erect, branchshrub up to 1 meterheight

2 Andrographispaniculata (Burm. f.)Wall. ex Nees

Kiriyath Acanthaceae Leaves An erect branchedannual herb with 0.3to 0.9 meter inheight

3 Adathoda vasica L. Adalodakam Acanthaceae Whole plant An erect shrub with3 meter in height

4 Aegle marmelos (L.)Corr.

Kuvalam Rutaceae Leaves Medium sizeddeciduous tree withup to 8 meter height

5 Anacardiumoccidentale L.

Parankimavu Anacardiaceae Whole plant A small tree withunlimited growth

6 Azadirachta indicaA. Juss.

Veppu Meliaceae Whole plant A medium sized treewith 15 – 20 m inheight

7 Boerhaavia diffusaL.

Thazhuthama Nyctaginaceae Leaves A perennial diffuseherb withprocumbentbranches

8 Cassia fistula L. Kanikonna Leguminosae Whole plant A small deciduoustree with 8 – 15 m inheight

9 Cephalandra indicaWight & Arn.) Naud.

Koval Cucubitaceae Wholeplants

A perennial tendrilclimber

10 Cantharanthusroseus L.

Savamnari Apocynaceae Whole plant An erect herbaceousannual plant

11 Eclipta alba (L.)Hassak.

Kayyonni Asteraceae Leaves An erect prostrateannual plant

12 Emblica officinalisGaertn.

Kattunelli Euphorbiaceae Fruits Small tree

13 Tinospora cordifolia(Willad.) Miers

Amrutu Menispermaceae Whole plant A large climber withsucculent stem

14 Terminalia bellirica(Gaertn.) Roxb.

Thanni Combretaceae Bark, Fruit Large tree

15 Ocimum sanctum L. Thulasi Lamiaceae Whole plant Small shrub

16 Tamarindus indicaL.

Puli Leguminose Fruit Large tree

17 Gymnema silvestreR.Br.

Chakarakolli Asclepiadceae Slendertwig andleaves

Climber

18 Mangifera indica L. Mavu Anacardiaceae Fruit, bark,Gum,leaves

Perennial tree

19 Murraya koenigi L.Spr.

Kariveppu Rutaceae Leaf Small tree

20 Scoparia dulcis L. Kalluruki Scophulariaceae Whole plant small herb21 Zizyphus jujube Mill. Thodali Rhamnaceae Leaves Weak stemmed

plant22 Ficus bengalensis

L.Peral Moraceae Seed Medium tree

23 Tragia involucrataL.

Kodithuva Euphorbiaceae Whole plant Herb

24 Pterocarpusmarsupium Roxb.

Venga Fabaceae Whole plant large tree

25 Dioscoria alata L. Kachil Dioscoriaceae Tuber Climber

Table. List of some important plants with anti diabetic potential

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to document the uses and also draw theattention of plant researchers andpharmacologist for future line of research.

ACKNOWLEDGEMENT

Authors are thanking to HOD, Departmentof Botany, N.S.S. College, Nilamel, KollamDist, Kerala for providing facilities to carryout the study.

REFERENCES

Brahman, M. and H.O. Saxena, 1989.Ethnobotany of Gandhamaradan Hills –some noteworthy, medicinal uses. Int. Cont.Rec. Adv. Med. Arom. Spice Crops, NewDelhi. (Abst.)

Dey, K.L., 1973. Indigenous drugs of India.Parma Primelane, Chronica Botanica, NewDelhi.

Devlin, M.T., 1997. Text book of Bio chem.,Wileyliss, Inc,NewYork, 4 Edn, 287.

Jain, S.K., 1968. Medicinal plants, NationalBook trust, New Delhi.

Jain, S.K., 1981. Glimpses of Indian Ethnobotany (Ed.) Oxford and IBH Publishing co,New Delh

Karthikeyani, T.P., 2003 . Ethnobotanicalstudies among Yanandis of SathyaveduMandal, Chittor District, Andhra PradeshPlant Archive 3(1): 21 -27.

Singh, A.K., Raghubanshi, A.S., J.S. Singh,2002. Medical Ethanobotany of tribals ofSonaghati of Sonbhadra distt, U.P., Journalof Ethnopharmacology, 81:31 -41.

Marles, R.J. and N.R. Farnsworth, 1995.Plant to patient an ethnomedicinalapproach Phytomedicine. 2:137 - 189.

Pandey, A. and A.A. Khan, 2005. Study ofethnomedicinal importance of plantsconserved by tribals of panna distt. PlantArchives, 5 (2): 565 -568.

Warrier, P.K.,Nambiar, V.P.K. andRamankutty, C. 1996. Indian MedicinalPlants Vol.1 and 11, Orient Longman Ltd,Madras.

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269ECO-CHRONICLE

LANDUSE / LANDCOVER MAPPING OF MARUDAIYAR BASIN, TAMIL NADUUSING REMOTE SENSING DATA

Balaselvakumar, S.1 Kumaraswamy, K.2 and Jawahar Raj, N.3

1 Department of Geography, Periyar E.V.R. College, Tiruchirappalli, Tamil Nadu2 Department of Geography, Bharathidasan University, Tiruchirappalli, Tamil Nadu

3 Department of Geology, National College, Triuchirappalli, Tamil Nadu

ABSTRACT

IAn attempt has been carried out to map the landuse and landcover categories of Marudaiyar basin,using remote sensing data. The total area of the basin is 625 sq. km. and it is located in the centralpart of Tamil Nadu. Landuse / landcover maps were generated and the areas were categorized intobuilt-upland, agricultural land, forest, waste lands and water bodies on the basis of NRSAclassification. Agricultural and wastelands were dominant in the present study, which were about427 sq. km. (68.28%) and 136.82 sq. km. (21.88%) respectively. The significance of such a studyin the formulation of management plans / development plans is also discussed.

Keywords: Land use, Land cover, Remote sensing, Interpretation, NRSA

INTRODUCTION

Land is the most important natural resource,which embodies soil, water and associatedplants and animals constituting totalecosystem. The growing population andhuman activities are increasingly puttingpressure on the limited land and soilresources for food, energy and several otherneeds (Mulder, 1979; Shai, 1980).Comprehensive information on the spatialdistribution of land use / land covercategories and the pattern of their changeis a prerequisite for planning, utilization andmanagement of the natural resources(Anderson James, 1979; Luong, 1993).Hence, a study of this sort is crucial informulating the management anddevelopment plans.

The information on land use / land coverpatterns, their spatial distribution andchanges over a time scale are perquisitefor making development plans (Cautamand Narayan, 1982 & 1985; Dhinwa et. al.,1992; Ibrahim and Loulou, 1994). Remotesensing, the latest advancement in space

technology has the capabilities to overcomethe shortcomings of the conventionalmethods. It makes a major technologicalbreakthrough in the method of acquiringinformation on land resources, agriculture,forestry, ocean resources and other studies(NRSA, 1989; Rao, 1991). The present studydescribes the various land use / land covercategories of the basin.

Study Area

The study area, a sub-basin of Cauvery, islocated in the central part of Tamil Nadu andis bounded by Chinnar basin on the northNandhiyar basin on the south, Pachamalaihill on the west and Coleroon River on theeast. It extends from 11002’ to 11015’ Northlatitude and 78048’ to 79015’ East longitude(Fig. 1). It covers an area of 625 sq. km. Theaverage annual rainfall of the basin rangesfrom 750 to 1000 mm. The area iscomposed of a series of plains, valleybottoms, undulating uplands and brokenchains of Eastern – Ghats viz., Pachamalaihills. The average height of Pachamalai hillis 100 m, but few of its peak rise above 1020


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m above MSL. The elevation of the basinranges from 250-400 meters.

The study area is extremely covered bysedimentary rocks, especially calcareousrocks (limestone, gypsecous sandstone,calcareous sandstone etc,). Alluvium,deposited by the Marudaiyar basin is foundto occur in the downstream area especiallyin areas were the river joins with theColeroon River. Lineaments are mostlyfound in the north-south direction. Theformation of anticlines and synclines aremostly identified in Perambalur block.Alluvium and black soils are mostlyoccupied in the entire basin. The southernparts of the study are mostly occupied byblack soil.


The study has made use of various primaryand secondary data. These include Surveyof India (SOI) topographic maps (58I/16 and58M/04 on 1:50,000 scale) and IRS LISS –III Geocoded data of 1:50,000 scale for July2006. The Indian Remote Sensing Satellite(IRS) data were visually interpreted by usingthe image interpretation elements such astone, texture, shape, pattern, association etc.Adequate field checks were made beforeascertaining / finalization of the thematicmaps.


Analysis of Landuse / Landcover byRemote Sensing Data

The various land use categories wereidentified and demarcated (Fig 2) using IRS- IC LISS III geocoded data, based on theimage interpretation elements. They aredescribed as below.

Built – up landThe built-up land refers to areas of humanhabitation developed for non-agriculturaluses like building, industrial structure,transport and communication network, andutilities in association with agriculture.These features can be identified with their

dark bluish green tone in the core and bluishtone on the periphery. They have a typicalcoarse and molted texture. These areas arealso associated with network of canals,roads, and railway lines. Apart from thesettlement, these were also identified withtheir typical size.

In the study area, two major settlementswere distributed, one (Perambalur) in thenorthwestern part and the other in the central(Ariyalur) part. Few smaller settlements,which represent the minor towns, were alsomapped and these include Siruvachur,Perali, Mattur, Rettipalayam etc. In the basinthe build-up land account for an area of10.42 sq. km., which is 1.66% of the basinarea (Table 1).

Agricultural landThese are the lands mainly used for farmingand for production of food and othercommercial and horticultural crops. With thehelp of satellite data, it is possible to identifythe various agricultural lands upto level II.The various categories of the agriculturallands identified in the study area aredescribed in detail.

a. Crop landThese include all the agricultural areas andthese could be identif ied by theircharacteristic red tone, regular shape of theagricultural fields, association with waterbodies, etc., The crop lands are found welldistributed throughout the foot hill zones andplain regions of the study area. The kharifcrops (paddy, groundnut and crop) arecultivated in the months of June, July andAugust. It is interesting to note that suchcrops are totally distributed in the northernpart of the region. The rabi crops, mostlypaddy, cholam, cumbu and maize arecultivated in the months of October,November and December. Both the cropsare spatially distributed all over the studyarea and cover an area of 391.38 sq. km.(62.62%).

b. Fallow landThese are the lands, which remain vacantwithout crop. These were identified by their

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Figure 1.

Figure 2.

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dark greenish tone, smaller size, regularshape and medium texture. In the studyarea, such fallow lands are to be found inthe central and southeastern portions. Inthe basin, fallow land occupies 27.43 sq.km., which is about 4.38% of the basin.

c. PlantationsThese include areas where crops such aspaddy, coconut, banana, etc. are cultivated.Such areas were identified form their darkred tone, smooth texture and associationwith the foot hill of study area. In the studyarea, such plantations are found in the areasof Sil lakkudi, Papananchery andReddipalayam and Vikvamangalam areasof the basin, which lie in the south and southeastern portion of the study area.Approximately the plantation-cropped areasoccupy about 8.00 sq. km (1.28%).

ForestForest, which comprises of thick and densecanopy of tall trees are differentiated by theirred to dark red tone and varying sizes. Theyshow irregular shape and smooth texture.These forest areas are found on west,northwest tracts. It is found on the hill slopeof Naranamangalam, Siruvachur and theplain region of Chittali, Sirukanpur, Kottarai,Poyyur and Reddipalayam. Based on thetonal and textural variations, the forests ofthe study area were divided into threecategories such as dense, open anddegraded.

In the study area, the various forest classes,their distribution and their characteristicsare described in the following sections.

a. Dense & Open forestDense forests have the characteristic of darkred tone, smooth texture and irregularshape on the satellite images. In the studyarea, such dense forest areas are found inthe west and northwestern areas viz,Naranamangalam and it, surroundingregions. These forests are found confinedto the higher and medium altitudinal areas.

Open forests are found in the west andnorthwestern part of the study area. It isassociated with the areas of dense forest.Such dense & open forests cover an areaof about 1.83 sq. km (.30%).

b. Degraded forestThe forests of this category cover an area of11.73 sq.km, occupying 1.87%. The scrubs,bushes and smaller trees are predominantin the forest. Taller trees are limited. Inremotely sensed data, such forests wereidentified by yellow tone. These forests arefound in lower altitudes of the hill areas andfound associated with other forests.

WastelandsLand, which in its present state does not oronly possesses limited ability to supportvegetation, is called wasteland (DudleyStamp 1954). Ravinous, rock, mining, stony

Sl.No.

Level I Level II Area inSq. km.

Percentage ofBasin area

1 Built up land Built up land 10.42 1.66Crop land 391.38 62.62Fallow land 27.43 4.38

2 Agricultural land

Plantations 8.00 1.28Dense & Open forest 1.83 0.33 ForestDegraded forest 11.73 1.87Land with scrub 37.10 5.93Land without scrub 6.51 1.04

4 Waste lands

Barren rocky 93.21 14.915 Water bodies River/ stream / lake/

Reservoir/ tank/ canal37.02 6.01

Table 1. Land Use and Land Cover Classification of Marudaiyar Basin

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and active depositional features areincluded in this category. In the study area,there are three categories of wastelands,which could be easily identifiable from thesatellite image.

a. Land with scrubThese include the uplands or high groundswith scrub. These lands are subjected todegradation or erosion and consist mainlyof thorny bushes. Such areas were identifiedfrom their yellowish tone and theirassociation with uplands, and their irregularshapes. These areas are found nearAriyalur, Kilamattur, Rengasumudhiram,Nochikulam and their surroundings. Thetotal area under this category is about 37.10sq. km. (5.93%).

b. Land without scrubThese land area is also found associatedwith higher topography and are formed bydegradation or erosion. It could be identifiedin the satellite data from its light yellow tone,and its association with the higher altitudes.The absence of vegetation distinguishesthis category form the earlier described one.In the northern part of Kalpadi, Alambadi andNaranamangalam, such areas are foundto occur. This category occupies 6.51 sq.km. (1.04%).

c. Barren rockyThese are rocky exposures of varyinglithology, often barren and devoid of soilcover and vegetation. They occur amidst hillforests as openings or scattered as isolatedexposures or loose fragments of bouldersor as sheet rocks on uplands and plains. Inthe study area, these lands appeared asbrownish in colour and they are irregular inshape. These barren rocky, stony andravenous and mining wastes are found insurroundings of the hilly regions. The barrenrocky areas cover 93.21 sq. km. (14.91%).

Water bodies

Both man-made and natural water features,covered with water, are included in thiscategory. i.e. rivers / streams, lakes, tanksand reservoirs. The water features

appeared black in tone in the satelliteimagery. The shallow water and deep waterfeatures appear in light blue to dark blue incolour. In the study area, a number of tanksare evenly placed all over the basin, exceptsoutheastern portion. The lakes and tankscan be delineated from the imagery.

Some of the tanks are covered by energyplants, scrubs and bushes. They appearas light red in tone. The area under waterbody in the basin covered 37.02 sq. km(6.01%). A few reservoirs are also found inthe northwestern hil ly area and insoutheastern parts of the study area.

CONCLUSION

The land use categories of the study areawere mapped with the help of IRS data. Theland use categories were demarcated viz.built-up lands, agricultural lands, forests,wastelands and water bodies. The built-uplands include towns and villages/ minortowns. The total area covered under thisland use category is about 11.00 sq. km.(Table. 1). In area under agriculture, it waspossible to identify the croplands, fallowlands and plantations. Agricultural areaswere found well distributed throughout thestudy area for the reason that most of thepeople are engaged in agriculture activities.The total area covered by this land usecategory is about 427 sq.km, out of 625 sq.km. of the basin. The forests of the studyarea are confined to the hill slopes ofNaranmanagalam, Siruvachur, Chittali,Sirukanpur, Kottarai, Poyyur andRedipalayam. The forests occupy about13.56 sq. km.

Wasteland categories, such as land with /without scrubs and barren rocky, stony,mining and ravinous areas weredemarcated. The land with / without scrubwere found near Naranamangalam,Rengasumudiram, Kalpadi and Alambadiareas, while the barren rock areas werefound in the vicinity of the hilly areas. Itoccupies an area of 136.82 sq. km. of thestudy area. In the water body category,features such as rivers / streams, tanks and

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reservoirs were delineated. In the study area,several streams / rivers representing theKrumbapalayam, Panangur, Kalpadi,Sattanur and Sirukanpur etc. were identified.The tanks were found well distributedthroughout the study area, except in thecentral area where they were less. A fewreservoirs were also identified in thenorthwestern hills of the study area, its coverabout 37.02 sq. km. The mapping oflanduse / landcover is useful for presentstatus of landuse analysis, planning anddecision-making process.

REFERENCES

Anderson James, R., 1979. “Land Use andLand Cover Changes: A Frame Work forMonitoring”, Journal of Research, UnitedStates Geological Survey (USGS), 5(2), pp.143-153.

Cautam, N. C. and L.R.A. Narayan, 1982.“Suggested National Land Use and LandCover Classification System for India usingRemote Sensing Techniques, PinkPublishing House, Mathura.

Cautam, N.C. and L.R.A. Narayan, 1985.“Land Use and Land Cover Mapping andChange Detection in Tripura using Land satSatellite Data”, Journal of Indian Society ofRemote Sensing, pp. 517-528.

Dhinwa, P. S., Pathak, S. K., Sastry, S. V. C.,Rao, M., Majumdar, K. L., Chotani, M. L.,Singh, J. P. and Sinha, R. L., 1992. “LandUse Change Analysis of Bharatpur District

using GIS”, Journal of the Indian Society ofRemote sensing, pp. 237-250.

Ibrahim, H. and Loulou, A.R., 1994. “RemoteSensing Application to Land use/ Land coverin Syria. Proc. Of the 15Asian Conference ofRemote Sensing, Vol.2, Bangalore.

Luong, P.T., 1993. “The Detection of LandUse / Land Cover Changes using Remotesensing and GIS in Vietnam”, Asian PacificRemote sensing Journal, pp. 63-66.

Mulder, J., 1979. “Integrating WaterResource and Land use Planning”, Logan,Utah Water Research Laboratory, Utah StateUniversity.

National Remote Sensing Agency, 1989.“Manual of Nation wide Land Use / LandCover Mapping using satellite imagery, Part– I, NRSA, Hyderabad.

Pathak, R.C. and J.M. Kate., 1988. “LandUse Mapping by Air photo Techniques,Journal of Indian Society of Remote Sensing,pp. 47-52.

Rao, D.P., 1991. “IRS IA Application for LandUse / Land Cover Mapping in India”, CurrentScience, pp. 153-167.

Shai, B., 1980. “Land Use Survey of IdukkiDistrict” - Technical Report Prepared Jointlyby the Kerala State Land Use Board,Trivandrum and Space Application Centre,Ahemedabad, p109.

275ECO-CHRONICLE

PHYSIOLOGICAL AND BIOCHEMICAL STRESSES OF COIR COLOURINGINDUSTRIAL EFFLUENTS (RODAMINE – B) ON VILLORITA CYPRINOIDES.

Arun, A. U.*

Department of Marine Biology, Microbiology and Biochemistry,School of Marine Sciences, Cochin University of Science and Technology,

Cochin, Kerala.

* Present Address: Department of Zoology, St. Peter’s College, Kolenchery, Kerala.

ABSTRACT

Very low concentration of coir colouring effluent is toxic to clam Villorita cyprinoides as the Lc 50was found to be 0.089 % (range 0.0835 to 0.0992). Even at low concentrations of effluent (0.04 %and 0.08 %) after 10 hr of dosing, consumption of oxygen was higher than the control in the initialphase (0 – 10 minutes) of exposure in freshwater. After 100 hr of exposure, clams exposed to allthree concentrations (0.04 %, 0.08% and 0.12 %) of effluent showed a sharp decrease in oxygenconsumption with increase in duration when they are exposed to freshwater. In 100 hr dosingexperiment there was a significant difference between periods (P<0.01) and concentrations(P<0.01). In all groups of 500 hour dosed organisms (0.04 %, 0.08 % and 0.12 % of effluents), therewas a significant increase in the rate of oxygen consumption during second phase (10 - 20minutes) and showed the minimum during last phase (20 - 30 minutes). In different groups of dosedorganisms (0.04 %, 0.08 % and 0.12 % of effluent), there was a significant increase in theaccumulation of lipid in the tissues compared to the control during 10 hour, 100 hour and 500-hourexposure. There was no significant difference between periods, but a significant differencebetween concentrations (P<0.001) are striking. The acute toxicity of this effluent on this speciesare quite invoking in view of the fact that the shellfishery of Cochin backwater has drasticallydeclined over last two decades. The depletion of shellfishery at this rate is expected to upset theecological balance of this vulnerable ecosystem and eradication of the species.Key words: Rhodamine-B, Coir colouring effluents, LC50, Oxygen uptake, Lipid storage.

INTRODUCTION

Villorita cyprinoides is a typical depositfeeding bivalve and occupies a position lowin the food chain. They (Plate.1) mainly feedon finely dispersed organic matter from thesurrounding water by ciliary action, whichserve both respiratory and feeding functions.Clams, like other bivalves, close their shellvalves and keep isolated duringunfavourable conditions as their f irstresponse (Bayne, 1973a and b).

Cellular metabolism in any organism isdependent on its inherent capacity to convertsimple compounds into complex forms forcellular functions. The energy for these

chemical processes comes from oxidationof high energy phosphate molecules, whichin turn, is dependent on the consumption ofoxygen during respiration. Hence therespiratory rate clearly represents themetabolic activity of the organism.Knowledge regarding the l imits ofrespiratory function is important forunderstanding the physiological adaptationof a species. The rates of oxygenconsumption by aquatic organisms vary withdifferent stress conditions, induced bynatural or anthropogenic action. Hencestudies on respiratory metabolism mayprovide an idea of the physiologicalcondition of an organism. Mainly two typesof responses to stress can be defined (i)


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Acute or short term response, occurringimmediately after exposure of an animal tostress (ii) Stabilized or long term responseon prolonged exposure to stress over days,weeks or months. Studies of short termresponses is relatively important, as theymay indicate the sequence of eventsleading to the stabilization of the metabolicrate and adaptive behaviour of the organism.

Significance of the study

Aquatic pollution generally refers todeleterious changes in the Physiologicaland chemical properties of water to one thatis dangerous to the survival of organisms.The effects of water pollution are not onlydevastating to people but also to aquaticbio-resources, including shell fishes andfinfishes. There are two basic forms of waterpollution; (1) changes in the types andamounts of materials introduced into water,and (2) alterations in the physicalcharacteristics of water. Agriculture activities,industrial activities handling of petroleumproducts, landfills, sanitary sewers, stormsewers, construction activities etc. canpollute the aquatic environment. Of these,wastewater from industries is a majorsource of water pollution, as they usuallycontain specific and readily identifiablechemical compounds. In fact, a number oflarge and medium-sized industriesencircling Cochin Estuary do not haveadequate effluent treatment facilities, andthey discharge effluents directly in to thebackwater without any treatment. Some ofthese include paper mills, distilleries,refineries, leather processing industries,coir industries, fertilizer industries, textileindustries etc. Among these, coir colouringindustries are of major concern in thecoastal belt of Cochin Estuary.

One of the most important aspects oftoxicology is the relationship betweenexposure concentrations, duration ofexposure, and the response of organism tothe medium. LC50 stands for theconcentration of a material require to killone-half of the animal population tested ina specified amount of time. Laboratory

studies focus on effects or responses oforganisms with continuous exposure to atoxicant or stressors. Majority of cases ofanthropogenic contamination of aquaticsystems occur by episodic discharges ofpollutants with in a short duration. This isespecially true in the case of effluents, wherethe day-to-day input of the pollutant is withinregulatory guidelines, but occasionally thereis a release that well exceeds permittedlimits. Usually, the 96 hr LC 50 values areused to fix sub-lethal concentration of anypollutant. Acute toxicity study becomes anintegral part of any laboratory based studyto understand sub-lethal effects of anytoxicant with reference to the metabolicactivity of an organism.

Rhodamine B (Plate 2) (CosmeticIngredient Solvent Red 49) is used as a dyefor imparting colour to coir, silk, cotton,wool, nylon, paper, etc. The molecularformula of Rhodamine-B isC28H31ClN2O3, with a molecular weightof 479.02. It is chemically known as 9-(2-Carboxyphenyl)-3,6-bis(Diethylamino)Xanthylium Chloride and Synonyms isTetraethyl Rhodamine Hydrochloride; D&CRed no. 19, C.I. Food Red 15, C.I. BasicViolet 10; C.I.# 45170. Rhodamine B alsohas been used as a tracer in water pollutionstudies (Cesark, 1970, in IARC, 1987). Untilthe late 1980’s, the FDA certif iedRhodamine B stearate (D&C Red No. 19)as a color additive in cosmetics and drugs(IARC, 1987), but this certification waseventually revoked (CTFA, 1991). Thesystemic toxicology of Rhodamine B hasbeen studied for acute, subchronic (IARC,1987), and chronic effects (Bio/Dynamics,1981b). It is established that Rhodamine Bhas toxic properties besides beingidentified as a carcinogen, as it is completelysoluble in water (Formulabs, 1988).However, its persistence in the environmentor its toxicity to nontarget terrestrial andaquatic species has not been well studied.It is the main component of red dye, widelyused for impregnating colour to coirproducts. This also forms the majorcomponent of the effluent from many coirfactories. It is estimated that about 200

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minor and major coir dying industries,located in the upper reaches of Cochinbackwaters, discharge a significant amountof effluents containing Rodamine-B into theestuary. Evaluation of the physiological andbiochemical impact of this pollutant is amajor requisite in order to find the reasonfor the depletion of clam fishery in theestuary. The present study is a pioneeringeffort to assess the toxic effect of coircolouring effluent (Rhodamine ) to the biotaof the estuarine area.

MATERIALSAND METHODSMaintenance of test animals in the laboratory

In the laboratory, the collected individualswere maintained in plastic tubs of 200 litrecapacity, containing constantly aeratedfreshwater and fed with the algae Chlorellafor a period of 24-36 hr before thecommencement of the experiment. Animalsused for the experiment have a shell lengthof about 25± 4 mm. All the experiments wereconducted under normal laboratoryconditions.

Experiments with effluents

The effluent selected for the study wascollected from the coir colouring factorysituated near Cherthala, south of Cochin,Kerala. Samples were collected from thefactory 1-2 days before the commencementof the experiments. The dye is normallyprepared by mixing 70gm of Rhodamine-B,small quantities of orange & yellow dyes,and 250 ml H2SO4 and 500 l boiled water.50 Kg of coir ropes were immersed in theboiling mixture. After one and half hours, thecoir ropes were taken out and a new setwas dipped for colouring. Nearly 300 Kg ofropes were thus coloured using 500 litresof dye mixture. After completing the work,these effluents were discharged in to thenear by water body. Even though there weresome minor differences in the preparationof dye mixture, the principal components inthe mixture were the same. It is to be notedthat the absorption maximum forRhodamine – B and effluents are different(Fig.1 and 2). The absorption peek of pureRhodamine – B was about 540 nm whereas

the absorption peek of dye mixture wasnearly 600 nm. Basic characteristics of theeffluents are given in Table.1.

Lethal toxicity study (LC 50)

The laboratory conditioned clams of size (25± 4 mm) were sorted out using VernierCallipers. Ten clams were exposed to 5 l oftest solutions in fiberglass tubs of 10 lcapacity with Perspex l ids. The testsolutions were changed every 24 hour,never aerated and but fed with chlorelladuring the experiment. Valve gaping beyond5 mm and /or inability of the clams to closethe valves under mechanical stimulationwere chosen as the indices of death. TheLC 50 levels and their 95 % confidence levelwere calculated using probit analysis.

Sub-lethal toxicity studies

The concentrations of the pollutants usedfor the sub-lethal study were computed fromthe 96 h LC50 experiments. Animals wereexposed to three different doses of dyeeffluent, 1) high doses (3/2 of LC 50 or 0.12 %of effluent dilution) for a period of 10 hr, 100hr and 500 hr, (2) moderate doses (LC 50concentration or 0.08 % of effluent dilution)for a period of 10 hr, 100 hr and 500 hr and(3) low doses (1/2 of LC 50 concentration or0.04 % of effluent dilution) for a period of 10hr, 100 hr and 500 hr. Test solutions werenever aerated but regular feeding wascarried out before renewing the water eachday. Clams were exposed to the solutioncontaining dye effluent in fiberglass tubs of5 litre capacity. After the exposure period,clams were subjected to oxygenconsumption and lipid estimation.

Estimation of Oxygen uptake

Modified Micro Winkler method (Stricklandand Parson 1968) was followed for theestimation of oxygen consumption.Introduced a healthy aquatic animal to a

Colour RedParticle size 50 – 60 µmDissolved Oxygen 1.48 mg/lP

H1.7

Table 1.

ECO-CHRONICLE278

Plate . Villorita ciprinoides

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

200 300 400 500 600 700wave length

abso

rban

ce

Fig. . Structure of the dye

Fig. . Absorption spectrum of Rhodamine - B

Fig. . Absorbance spectrum of effluent

Plate . Villorita ciprinoides

PlateCoir dye effluent

279ECO-CHRONICLE

measured volume of fresh water and pouredan even layer of liquid paraffin wax over thewater to prevent further dissolution ofatmospheric oxygen to it. Dozed clams (0.04%, 0.08% and 0.12% effluents) were placedin two litre conical flask with one litrefreshwater and dissolved oxygen wasmeasured at 10, 20 and 30 minutesintervals. Difference between the dissolvedoxygen concentrations of first and secondgives the oxygen consumption of thatorganism during that time interval.

Lipid estimation

The method of Barnes and Blackstock(1973) was used to estimate lipid levels.After exposing the clams in differentconcentrations of effluents, the clams werecut open and wet tissues were taken out,weighed, extracted with chloroform –methanol mixture and the tissue extract wastreated with sulphuric acid, phosphoric acidand vanillin. The optical density of the red-coloured complex was measured at 520 nm.

Statistical analysis.

Two way ANOVA was used to analyse thecorrelations of oxygen consumption andlipid accumulation. Probit analysis wasused to determine the LC 50 and confidencelimit.

RESULTS

LC 50

Initial dosing experiments showed thatthese effluents were highly toxic to theorganism. Up to 4 % of the effluent dilution,all the clams died within 48 hours ofexposure. Experiments were carried outwith lower concentration to find out the LC50 of the effluent. From the Probit analysisLc 50 was found to be 0.089 % (range0.0835 to 0.0992).

Respiration in dosed organisms

After 10 hr of dosing, organisms exposedto 0.04 % and 0.08 % of effluent had higherrate of consumption of oxygen than that ofcontrol in the first 10 minutes (Graph.1). Asthe time progressed (ie., after 20 min and

30 min) the consumption of oxygendecreased in clams exposed to 0.04 % and0..08% effluent concentrations, but in 0.12% no such trend was noticed. Averageoxygen consumption rate in control was0.3418 mg O2.hr-1.wbwt-1, in 0.04 % it was0.1805 mg O2.hr-1.wbwt-1, in 0.08 % it was0.2236 mg O2.hr-1.wbwt-1 and in 0.12 % itwas 0.0319 mg O2.hr-1.wbwt-1.

After 100 hr of exposure, clams exposed toall the 3 concentrations of effluents showeda sharp decrease in oxygen consumptionwith increase in duration. As compared tocontrol, clams exposed to all concentrationsshowed sharp decrease in the consumptionof oxygen after initial 10 minutes (Graph.2).Average oxygen consumption in control was0.6066 mg O2.hr-1.wbwt -1, in 0.04 % ofeffluent dilution it was 0.1868 mg O2.hr-

1.wbwt-1, in 0.08 % it was 0.095 mg O2.hr-

1.wbwt-1 and in 0.12 %, it was 0.1868 mgO2.hr-1.wbwt-1. There is significant differencebetween periods (P<0.01) and betweenconcentrations (P<0.01).

In 500 hour dosed organisms of 0.08 %and 0.12 % of effluent concentrations therewas a signif icant increase in oxygenconsumption rate during second phase (10- 20 minutes), and it decreased during next10 minutes to the minimum towards the lastphase (20 – 30 minutes)(Grah.3). Averageoxygen consumption showed that in controlit was 0.4824 mg O2.hr-1.wbwt-1, in 0.04 % itwas .4313 mg O2.hr-1.wbwt-1, in 0.08 % it was0.4348 mg O2.hr-1.wbwt-1 and in 0.12 % itwas 0.3614 mg O2.hr-1.wbwt-1

Lipid in dozed organisms

In all groups of dosed organisms (0.04 %,0.08 % and 0.12 % of effluent dilution) therewas an increase in the amount of lipid inthe tissue, compared with the control during10 hour, 100 hour and 500 hour exposureto effluent. In 0.04 %, 0.08% and 0.12 %dilution of effluents, there was an increasein the concentration of lipid in all dosedorganism up to 100 hr with respect to control(Graph.4,5, &6). Compared to 10 hr dosedorganism, 100 hour dosed organism

ECO-CHRONICLE280

showed higher level of lipid. In the case ofcontrol organisms, as the time of exposureincreased, the lipid accumulation in thetissue decreased. There was no significantdifference between periods, but betweenconcentrations there was signif icantdifference (P<0.001). The 10 hr and 100 hrgave significantly higher values than 500 hr.

DISCUSSION

It is of value in studying the effects of waterpollution on commercial ly importantshellfish. The amount of water that passesover the gil ls of lamell ibranchs is of

considerable interest in the study ofnutritional, respiratory or excretory activitiesof these animals. LC 50 value for the dyeeffluent was found to be 0.089 %. Thephysico-chemical nature of the dye in papermill effluent showed the presence of highamounts of solids, BOD, COD, sodium,magnesium, calcium, chloride, ammonium,nitrogen, sulphate and absence of DO,probably causing an osmotic imbalance inthe aquatic organisms (prawns) which leadto early mortality ( Rahul Kundu et al., 1989).Another significant reason for the low LC50(0.089 %) may be the change in the toxicantduring the process of coir colouring, ie., the

Graph.1

Graph.3

Rate of respiration- 10 Hour

00.05

0.10.15

0.20.25

0.30.35

0.4

control 0.04 per. 0.08 per. 0.12 per.effluet dosage(percentage)

mg

O 2/hr/g

m w

etbo

dywt

Rate of respiration - 100 Hour

0

0.2

0.4

0.6

0.8

control 0.04 per. 0.08 per. 0.12 per.

effluent dosage(percentage)

mg

O 2/h

r/gm

wet

bod

ywt

.

Graph.2

Rate of respiration- 10 Hour

00.05

0.10.15

0.20.25

0.30.35

0.4

control 0.04 per. 0.08 per. 0.12 per.effluet dosage(percentage)

mg

O 2/hr/g

m w

etbo

dywt

Rate of respiration - 100 Hour

0

0.2

0.4

0.6

0.8


effluent dosage(percentage)

mg

O 2/hr/g

m w

et b

ody

wt.

Graph.3

Rate of respiration-500 Hour

0

0.1

0.2

0.3

0.4

0.5

0.6


Effluent dosage ( percentage)

mg

O/hr

/gm

wet

bod

y wt

Concentration or lipid - 10 Hour

0.225

0.23

0.235

0.24

0.245

0.25

0.255

0.26

Control .04% effl .08% effl .12% effleffluent concentration (percentage)

mg/

1 m

g tis

sue

Graph.4

Rate of respiration-500 Hour

00.10.20.30.40.50.6

control 0.04 per. 0.08 per. 0.12 per.Effluent dosage ( percentage)

mg O

/hr/gm

wet

body

wt

Concentration or lipid - 10 Hour

0.2250.23

0.2350.24

0.2450.25

0.2550.26

Control .04% effl .08% effl .12% effleffluent concentration (percentage)

mg/ 1

mg t

issue

Graph.5

Concentration of lipid - 100 Hour

0.210.220.230.240.250.260.27

Control .04% effl .08 % effl .12 % effl

efflunet concentration (percentage)

mg/

1mg

of ti

ssue


0

0.05

0.1

0.15

0.2

Control .04% effl .08 % effl .12 % effleffluent concentration(percentage)

mg

/1 m

g of

tiss

ue

Graph.6


0.210.220.230.240.250.260.27

Control .04% effl .08 % effl .12 % effl

efflunet concentration (percentage)

mg/

1mg

of ti

ssue


0

0.05

0.1

0.15

0.2

Control .04% effl .08 % effl .12 % effleffluent concentration(percentage)

mg

/1 m

g of

tiss

ue

281ECO-CHRONICLE

absorption peak of dye is at 550 nm andthat of the effluent is at 600 nm. This shift inabsorption spectrum revealed that theRodamine – B might have changed into newsubstance during the process of coircolouration. According to Shakthivel (1989)96 hour LC 50 values of textile mill effluents(Dye) were 10.8 % and 8.4% for Cyprinuscarpio and Thilapia mossambicusrespectively. Very low LC 50 in clams (0.0893%) observed in this study show thatorganisms are highly sensitive to the dye,and hence, a strict regulation of theseeffluents into the backwaters is warranted.

Rate of oxygen consumption has been usedas a valuable tool to study the sublethaleffects of pollutants (Prabhudeva andMenon, 1986; Mohanan et al., 1986). It offersa useful method to assess stress since itis an index of energy expenditure to meetthe demands of environmental alteration.In this study, as the concentration of the dyeincreased, the rate of consumption of oxygenalso decreased. Similar observations havebeen reported by Mathew (1990) in Donaxincarnates under heavy metal stress (Hgand Cu). Brown and Newell (1972)concluded that the reduction in respirationin Mytilus in the presence of copper wasdue to suppression in the ciliary activity andclogging of gills by the particles present inthe dye effluent ( 50 to 60 µm). Consumptionof oxygen for dosed organisms (0.04%, 0.08% and 0.12 %) were always less than thatof control. Similar results were obtained byBaby (1987) in Perna indica dosed withheavy metals. At low concentration (0.04 %),as the period of dosage increased (10 hr,100hr and 500 hr), consumption of oxygenalso increased. In all categories of dosages(0.04 %, 0.08 % and 0.12 %) after 10 hour ofexposure of toxicant, there was a suddendecrease in the oxygen consumptioncompared to control, due to metabolicinhibitions during early dosage period.Hawkins et al.(1986) made this observationin Perna indica, when it is has subjected tosalinity stress. According to Srivastava et al.(1995a) disturbances in carbohydratemetabolism and respiration have beenobserved in catfish after exposed to

Malachite green.

It is well known that bivalves are able towithstand periods of lack of oxygen throughshell closure (Davanport, 1984). Thechange over from aerobic to anaerobicrespiration in bivalves normally occurswhen the oxygen tension of the mantle cavityfalls leading to closure of valves in responseto environmental stress. Bivalves are calledfacultative anaerobes, capable of survivingduring oxygen stress condition andactivating non-oxidative metabolism in itsabsence(Hochachka, 1985). In thisorganism under anoxic conditions, organicsubstrates instead of oxygen, act as theacceptors of electrons (Karnaukhov, 1917).According to Hochchka and Somero (1973)these substances can be fatty acids. It iswell established that some components ofl ipids such as vitamin K, E and â-carotenoids are known to act as electronacceptors and antioxidants. In the presentstudy, i t is observed that as theconcentration of effluent increased, theaccumulation of lipids also increased ie.,there is a positive correlation betweeneffluent concentration and lipid. Accordingto Murugesan and Haniffa (1994) there wasan accumulation of lipid in tissues whenMacropodus cupanus (Cuvier) was exposedto textile mill effluents. Total cholesterol levelof blood increased significantly at all theconcentrations of malachite green inrespect to all the time intervals (Srivastavaet al., 1995 b).

It is found that coir colouring effluent ishighly toxic to the aquatic organisms evenat very low concentration, which leads to themortality of the species. It is also noted thatthe place where the discharge of the dyeeffluent is taking place (small canals orwater bodies) is devoid of aquaticorganisms especially the benthic speciesevincing that these effluent receiving areasremain biologically deserted.

REFERENCES

Baby. K.V., 1987. Combined toxicity of Heavymetals and Petroleum Hydrocarbons onSelected Marine Organisms. Ph.D. Thesis,

ECO-CHRONICLE282Cochin University of Science andTechnology, Cochin, India.

Barnes, H. and J. Blackstock., 1973.Estimation of lipids in marine animals andtissues: Detailed Investigation of thesulphophosphovanillin method for totallipids. J. Exp. Mar. Biol. Ecol., 12: 103 - 118.

Bayne, B.L., 1973a. Physiological changes

in Mytilus edulis L- induced by temperatureand nutritive stress. J. Mar. Biol. Ass. U.K.53: 39 - 58.

Bayne, B.L., 1973b. The response of threespecies of bivalve mollusk to decliningoxygen tension at reduced salinity. Comp.Biochem. Phy. 45: 793 - 806.

Bio/Dynamics, INC.1981 b. Life time feedingstudy of glyphosphate (Roundup Technical).Project No. 77-2012 for Monsanto Co., St.Louis, M.O.EPA Accession Nos. 246617 and246621. (Cited in U.S.E.P.A .1992 a).

Brown, B. and Newell, R.C., 1972.The effectof copper and zinc on the metabolism ofMytilus edulis. Mar. Biol.,16:108 -118.

Cesark, F.F., 1970 Xanthene dyes In KirkR.E, Othenner DF (eds.) – Encyclopedia ofChemical Tech.2ed.Vol.22, New York, JohnWiley and Sons.P.434,436.

CTFA : http://www.ctfa.org/

Davenport,J. and Redpath,K.J.1984.Copper and the mussel Mytiilus edulis L.in: Toxins, Drugs and Pollution in MarineAnimals. Bolis,L., Zadunaisky, J. and Gills,R. (ed.), Springer-Verlag, Berlin.176-189.

Formulabs. 1988. http://ww.turnerdesigns.com/t2/doc/appnotes/998-511.html.

Hawkins,A.J.S., Bayne,B.L.,Menon,N.R. andDamodaran,R. 1986. The mussels Pernaviridis and Perna indica has transplantableindicators of pollution: comparison of theirmetabolic response to reductions of bothoxygen tension and salinity.: Nationalseminar on mussel watch, 1:51-62.

Hochachka,P.W.1985. Theorms ofEnvironmental adaptations. C.M.F.R.I.,Special publication No.26, Cochin, India: 153.

Hochachka,P.W.and Somero, G.N.1973.Strategies of Biochemical adaption. W.B.Saunders. Philadelphia.358.

IARC: http://www.iarc.fr/

Karnaukhov, V.N. 1917. Dessertaion.Institute of Biophysics. Acad. U.S.S.R.Pushchino, U.S.S.R. Russia.

Mathew Philip., 1990. Sub-lethal effects ofheavy metals on Perna indica (Kuriakoseand Nair) and Donax incarnates Gmelin.Ph.D Thesis submitted to the CochinUniversity of Science And Technology.

Mohan, C.V., Gupta,T.R.C., Shetty,H.P.C. andMenon,N.R. 1986. Combined toxicity ofMercury and cadmium to the tropical greenmussel Perna viridis. Dis.Aquat.org.,2:65-72.

Murugesan, A.G. and Haniffa, M.A., 1994.Influence of textile mill effluent on foodutilization of the freshwater fish Macropoduscapanus. Environ. Ecol.,12 (1) :195 - 198.

Prabhudeva, K.N. and Menon, N.R. 1986.Oxygen consumption under copper and zincstress in Perna viridis. Fish. Technol., 23(1): 24-26.

Prabhudeva, K.N. and Menon, N.R. 1986.Oxygen consumption under copper and zincstress in Perna viridis.Fish.Technol., 23(1):24-26.

Rahul- Kundu., Prasad, V.V.S., Mansuri, A.P.,1989. Studied the toxicity of diluteddyeingand printing industry effluent to a penaeidprawn, Parapenaoepsis sculptilis(Hellen).Acta-hydrochem.-hydrobiol,17(1) ; 87-93.

Sakthivel, M., 1989 . Toxic effects of tanneryand textile mill effluents on the fishesCyprinus carpio and Oreochromismossambicus. Environ. Ecol. 7 (3); 685 -689.

Srivastava, A.K., Sinha, R., Singh,N.D., Roy,D. and Srivastava,S.J. 1995b. Malachitegreen induced changes in carbohydratemetabolism and blood cholesterol levelsin the freshwater catfish Heteropneustesfossilis. Acta.Hydrobiol.37(2),113 – 119.

Srivastava, S.J., Sing,N.J., Srivastava, A.Kand Sinha,R. 1995a. Acute toxicity ofmalachite green and its effect on certainblood parameters of a cat fish,Heteropneustes fossilis. Aquat. Toxicol. 31,241-247.

Strickland, J.D.H. and Parsons, T.R., 1968.A manual of seawater analysis. Bull No.167(Fishery Research Board Canada).

283ECO-CHRONICLE

REMOTE SENSING AND GIS TECHNIQUES FOR EVALUATION OFGROUNDWATER QUALITY INDEX IN MALATTAR SUB- WATERSHED,

GUDIYATTAM BLOCK, VELLORE DISTRICT, TAMIL NADU

Amutha, R 1., Porchelvan, P 1. and Poovalinga Ganesh, B 2.

VIT University, Vellore, Tamil Nadu.PRIST University, Tanjore, Tamil Nadu

ABSTRACT

The present study is an attempt to monitor the ground water quality in relation to land use / landcover and interpolated maps using remote sensing and GIS techniques for Malattar sub- watershed,Gudiyattam Block, Vellore District, Tamil Nadu, India. Thematic maps such as land use and landcover for the study area were prepared by using visual interpretation and supervised classificationtechniques with base map of SOI toposheets and IRS P6 LISS IV data on 1:50,000 scale using ArcGIS 9.1 and ERDAS 8.6 software. The study area constitutes different land use / land cover. 31%of the area is occupied by agricultural land, 11% area of cropland, 9% area of fallow and harvestedland, 37% area covers forest land, and remaining 12% of the area occupied by others such aswater bodies hills, settlements, uplands with scrub and tanks. Physico-chemical analysis data forthe groundwater samples were collected by grid pattern method at predetermined 34 locationsformed as non spatial database for the study. A relationship between land use / land cover andmechanism of controlling ground water chemistry were determined by Gibbs plot and concentrationof major cations and anions in the ground water were vary spatially and temporally identified by thePiper plot. From this analysis, majority of the ground water samples were in the CaCl type and mixedCaMgCl type and in the anion triangle field samples were in the SO4 and Cl which leads aciditycharacters to the ground water. Also, alkaline earth mineral of Ca and Mg exceeded the alkaliminerals of Na and K and strong acids Cl and SO4 exceeded week acids HCO3. WQI for the studyarea results shows that the area has poor water quality and treatment necessarily requires inexisting unconfined aquifer regions.Keywords: Ground water quality, WQI, Land use / Land cover, remote sensing & GIS.

INTRODUCTION

Ground water is an integral component ofthe natural cycle. It occurs as an aquiferlayer within a consolidated geologicalsubstratum. The main sources of groundwater recharge are precipitation and streamflow (influent seepage) while those ofground water discharge are eff luentseepage into streams, lakes, springs,evaporation and pumping (Gupta, 1991).Groundwater quality study has a specialsignificance and needs great attention ofall concerned since it is a major alternatesource of domestic, industrial and drinkingwater supply and irrigation all over the world.In the last few decades, there has been a

tremendous increase in the demand forfresh water due to rapid growth of populationand the accelerated pace of industrialization(Tiwari and Nayak, 2002.) Further, thegroundwater and the pollutants that it maycarry, move with such a low velocity, that itmay take considerable t ime for thecontaminants to move away from the sourceof pollution, and degradation in thegroundwater quality may remain undetectedfor years. Once the groundwater iscontaminated, its quality cannot be restoredby stopping the pollutants from the source(Purandara and Varadajan, 2003). Thequality of ground water is equally importantas that of quantity. Remote sensing and GISare effective tools for water quality and land


ISSN:0973-4155

ECO-CHRONICLE284

INDIA

VELLORE BLOCK

TAMIL NADU

Figure 1.Study area of the Malattar Sub-watershed with code

Figure 2. Landuse / land cover of the Malattar Sub-watershed

285ECO-CHRONICLE

cover mapping and are essential formonitoring and modeling environmentalchanges (Skidmore et.al, 1997). WaterQuality Index (WQI) was developed by theNational Sanitation Foundation (NSF) in1970 (Brown et.al., 1970) and wasdeveloped to provide a standardizedmethod for comparing water quality. Thepresent study is an attempt to monitor theground water quality in relation to land use/ land cover and interpolated maps usingremote sensing and GIS techniques forMalattar sub- watershed, Gudiyattam Block,Vellore District, Tamil Nadu, India. Gibbsplot, Piper’s diagram and Water QualityIndex (WQI), were worked out to assess thegeneral hydro geochemical characteristicsof groundwater in addition to chemicalanalysis to evaluate its suitability for drinkingpurposes.

STUDY AREA

The study area is Malattar sub-watershed amajor tributary of Palar River. The area liesbetween north latitude 78º39’ to 79º56’ andeast longitude from 12º48’ to 12º56’. It coversan geographical area of 163Sq.Km andfalls on part of Survey of India toposheets of57L/9 and 57L/13 (Figure 1).

Malattar River originates in the hilly regionsof Venkatagrikotta in Andra Pradesh andflows through Niakeneri forest of PalamanarThaluk. This river confluences Palar river, 5Km east of Ambur near Sathampakkamvillage on the left side and flows throughPernampet block of Vellore District. Themain tributaries of Malattar Rivers are,Duggammaeru, Dandapaner venke,Gittargunta venka, Batavenka, Gooddarvenka, Garisala venka and Kattar River. Thewatershed experiences tropical monsoonclimate with normal temperature, humidityand evaporation throughout the year. Themonsoon season in the watershed is fromJune to December. The annual rainfall isabout 517.44 mm. The rainfall occurrenceduring October and November is heavyresulting in significant amount of runoffoccurrences in the watershed. The rainfallstation is at Modikuppam near Gudiyattam,

in general, Geology of the study areapredominantly constitutes the fissi lehornblende biotite gneiss, alluviam (recent)and dykes. Major lineaments are found inthe central part of the area and flowingthrough NW to SE and W to E.


Survey of India toposheet was used todelineate the Malattar Sub-watershedboundary and IRS P6 LISS IV satellite datato map the distribution of current landuse\land cover with the aid of ARC GIS 9.1and ERDAS Imagine 8.6 software. Thirtyfour water samples were collected duringthe pre-monsoon period from both borewells and open wells in the study area andanalyzed for 14 major physicochemicalparameters such as pH, EC, Cl, HCO3, CO3,SO4, NO3, F, Na, K, Mg, Ca, Fe and TDS.Highly temperature depended physicalparameters such as pH and electricalconductivity were determined in the field atthe time of sample collection. Gibbs andPiper diagram were plotted with thesechemical parameters to identify thechemistry of ground water andconcentrations of major cations and anionsby using Rock Works (V.7.11.26) software.Water Quality Index (WQI) was computedaccording to B.I.S standards to identify thewater quality distributions in this region with10 parameters as given in table1.

SPATIAL DATA BASE

Malattar sub-watershed boundary withwatershed code, land use / land cover,geology, geomorphology maps are spatialdata base for the study. The area constitutesdifferent land use / land cover. 31% of thearea is occupied by agricultural land, 11%area of cropland, 9% area of fallow andharvested land, 37% area covers forest land,and remaining 12% of the area occupied byothers such as water bodies hil ls,settlements, uplands with scrub and tanks(Figure 2). Geology of the study area ispredominantly constituted by the fissilehornblende biotite gneiss, alluvium (recent)and dykes. Major interconnected

ECO-CHRONICLE286

1

10

100

1000

10000

100000

0.0 0.2 0.4 0.6 0.8 1.00.0 0.2 0.4 0.6 0.8 1.01.0

(Cl/Cl+HCO3)(Na+K)/Na+K+Ca

TDS

1 2

3

4

6

5

1 – CaHCO3 Type2 – NaCl Type3 – Mixed CaNaHCO3

4 – Mixed CaMgCl5 – CaCl Type6 – NaHCO3 Type

Figure4. Piper Plot

Figure 3.Gibbs plot for pre-monsoon

Figure 5. WQI spatial interpolation map ofthe Malattar Sub watershed

Figure 4. Piper Plot

287ECO-CHRONICLE

lineaments are found in the central part andwestern part of the area, which is flowingthrough NW to SE and W to E.Geomorphology of the area dominantlyconstitutes pediments deep, shallow, floodplain, structural hill and bazada. Thesegeological and geomorphological featuresare directly controlling the occurrences ofaquifer regions and ground water chemicalcharacters.

NON SPATIAL DATA BASE

Determination of WQIThe main objective of Water Quality Index isto turn complex water quality data intoinformation that is understandable andusable to the public. Water Quality Indexbased on some very important parameterscan provide a simple indicator of waterquality. It gives the public a general idea ofthe possible problems with water in aparticular region (Shankar and LathaSanjeev, 2008). This paper attempts toevaluate the water quality indices from theviewpoint of suitability of water for humanconsumption. The 10 parameters chosenfor the present study and their standardsrecommended by the BIS are shown in table1. The method of computing WQI has beenbriefly discussed here. In the first place, themore harmful a given pollutant of water, thesmaller in magnitude is its standard fordrinking water. So the unit weight Wi for theith parameter Pi is assumed to be inverselyproportional to its recommended standardSi (i = 1, 2, . . . , n) and n = no. of parametersconsidered = 14 in the present case).Thus, Wi = K/Si ——- (1)where the constant of proportionality K hasbeen assumed to be equal to unity for thesake of simplicity. These unit weights, Wi,for the 10 water quality parameters usedhere are shown in the last column of Table1, where pH has been assigned the sameweight as chloride. The quality rating qi forthe ith parameter P is given, for all otherparameters except pH, by the relation:qi =100 (Vi/Si) ——(2)where Vi is the observed value of the ithparameter, and S is its recommendedstandard for drinking water. For pH, the

quality rating qpH can be calculated fromthe relationqpH = 100[(VpH ~7.0)/1.5] —— (3)where VpH is the observed value of pH andthe symbol “~” means simply the algebraicdifference between VpH and 7.0. Finally, theWQI can be calculated by taking theweighted arithmetic mean of the qualityrating qi, thus,WQI = [(qi Wi)/Wi] —— (4)where both the summations are taken fromi =1 to i = 34 (the total no. of parametersconsidered).


The sampling of groundwater sampleswere carried out based on the grid patternmethod, to cover all the land use and landcover features of the study area. The groundwater samples were alkaline in nature andlow EC values were noted during the pre-monsoon season ranging from 45µs/cm to71µs/cm with an average value of 63.38µs/cm. Total dissolved solids were observedin the permissible limit ranges from 423mg/l to 674mg/l with an average value of577.9mg/l. HCO3 represents the major sumof alkalinity and exhibits very less values

Parameter (Pi) BIS Std.(Si)

Unitweight (wi)= K/Si

pH 6.5–8.5 0.004

Total hardness(TH)

300 0.003

Calcium (Ca2+

) 75 0.013

Magnesium(Mg

2+)

30 0.033

Chloride (Cl-) 250 0.004

Nitrate (NO3-) 45 0.022

Sulphate (SO42-

) 200 0.006TDS 500 0.002Fluoride (F

-) 1 1

Iron (Fe2+

) 0.3 3.33? wi 4.417

Table1. BIS Standards for water qualityparameters with their unit weights

ECO-CHRONICLE288

from 7 to 15 at pH between 6 to8. Anionswere in the increasing order ofCl>SO4>No3>HCO3>F. Cl was higher in pre-monsoon season indicating leaching fromthe upper soil layers due to industrial and

Sl.No.

Parameter (Pi) pH TH Ca Mg Cl NO3 SO4 TDS F Fe Subindex(qiwi)

WQI =?qiwi/?wi

1 Ithampattu 6.7 231.31 35 35 245 15 213 595 0.1 0.2 183.27 41.49

2 Sathampakkam 7.5 185.80 25 30 256 16 238 599 0.05 0.1 155.50 35.20

3 Bavarthampattai 7 198.27 30 30 226 14 267 614 0.05 0.1 122.11 27.65

4 Kothakuppam 6.5 173.33 20 30 289 15 245 624 0.05 0.1 122.13 27.65

5 Machampatu 6 157.12 30 20 234 13 245 584 0.05 0.1 132.22 29.93

6 Kailasagiri 6.5 165.22 25 25 256 14 234 588 0.05 0.2 177.01 40.08

7 Nariyampattu 6 210.74 35 30 234 16 245 601 0.1 0.1 127.52 28.87

8 Melkothakuppam 5.5 173.33 20 30 267 17 256 636 0.1 0.1 160.80 36.40

9 Melmurugai 6.5 173.33 20 30 278 18 267 644 0.1 0.1 171.74 38.88

10 Rajakoil 6 119.71 15 20 245 19 234 554 0.1 0.1 148.29 33.57

11 Sendathur 5.8 173.33 20 30 235 12 267 603 0.03 0.2 208.94 47.30

12 Kothamarikuppam 6 177.69 30 25 287 13 269 669 0.2 0.1 136.88 30.99

13 Karkur 6.5 210.74 35 30 279 14 256 651 0.1 0.1 127.41 28.85

14 Pallalakuppam 6.7 140.29 15 25 245 15 249 590 0.2 0.1 136.34 30.87

15 Balur 6.5 155.88 13 30 213 13 245 540 0.4 0.2 212.24 48.05

16 Kembasumuthiram 6 188.92 18 35 216 14 267 582 0.4 0.5 601.68 136.22

17 Melpatti 6.5 170.09 22 28 256 15 234 590 0.4 0.5 600.85 136.03

18 Chembedu 6 208.24 34 30 234 16 215 566 0.4 0.5 601.39 136.15

19 Pangarishikuppam 5.8 202.51 35 28 145 17 287 551 0.4 0.5 601.35 136.15

20 Valathur 5.9 210.62 30 33 287 18 267 674 0.05 0.5 567.06 128.38

21 Varathapalayam 6 177.69 30 25 214 14 256 584 0.05 0.5 565.74 128.08

22 Kilpatti 6.5 188.29 26 30 189 12 123 423 0.05 0.5 565.50 128.03

23 Redimakuppam 6 228.82 34 35 245 14 154 530 0.05 0.5 566.69 128.30

24 M.V.Kuppam 6.7 230.57 38 33 198 16 134 460 0.03 0.5 564.28 127.75

25 chokarasihikuppam 6.7 218.85 30 35 177 13 198 497 0.06 0.5 567.38 128.45

26 Gudanaragam 6.5 160.86 15 30 190 15 138 433 0.06 0.5 566.48 128.25

27 Melathur 5.5 193.91 20 35 278 16 234 607 0.06 0.5 567.96 128.59

28 pattu 6 144.65 25 20 287 17 234 611 0.06 0.5 566.28 128.20

29 Singlepadi 6 116.59 22 15 256 14 145 484 0.04 0.5 563.14 127.49

30 Sithathour 6 167.72 26 25 278 16 257 627 0.07 0.5 567.88 128.57

31 Kulidhagai 6.5 119.21 28 12 257 14 269 603 0.02 0.5 561.20 127.05

32 Bojanapuram 6.7 148.89 30 18 225 12 217 534 0.02 0.5 561.54 127.13

33 Ulli 6.5 144.65 25 20 247 10 218 562 0.02 0.5 561.67 127.16

34 Ananganullure 6.5 185.80 25 30 245 9 287 640 0.01 0.5 562.00 127.24?WQI 87.91

domestic activities and dry climates. SO4was higher than the desirable limit and fallswithin the acceptable limit in pre-monsoonseason indicating breaking of organicsubstances from topsoil / water (Miller 1979,

Table2. Results of physico-chemical analysis of groundwater samples

289ECO-CHRONICLE

Craig and Anderson, 1979). Nitrate andfluoride were within the permissible limit inpre-monsoon indicating leaching of fluoriderich rocks and organic substances fromweathered soil. In case of cations, Ca andMg was higher in pre-monsoon, indicatingthe weathering from primary mineralsources such as fissile hornblende biotitegneiss and dykes. Ca ranges from 13mg/lto 38mg/l and Mg ranges from 12mg/l to35mg/l. sodium is the most abundant alkalimetal which is highly mobile and soluble inground water and it ranges from 10mg/l to25mg/l with an average value of 17.53mg/l.This indicates that the highest soluble rocksare in the upper part of ground water.Potassium was also lesser in ground waterdue to its higher solubility (Herman Bouwer,1978) and it ranges from 7mg/l to 15mg/l.The dominance of cations were in the orderof Ca>Mg>Na>K>Fe. High mobile andsolubility of cations of Fe concentration ishigher in amount and its ranges from0.1mg/l to 0.5mg/l.

Gibbs (1970) proposed a method foridentifying relationship between watercomposition and mechanism controllingchemistry of ground water. (Na+K)/Na+K+Ca Vs TDS plot shows that the watersamples were fal ls within the rockdominance and Cl/Cl+HCO3 Vs TDS showsthat samples falls outside the plot as shownin figure.3. It could be by external influenceson ground water by industrial effluents andanthropogenic factors. Thus the majorground water chemistry results wereinfluenced by the underlying rock charactersthan the precipitation and evaporation.

The geochemical evolution of ground watercan be understood by plott ing theconcentration of major cations and anionsin the Piper Trilinear diagram (Figure4).Majority of the ground water samples werein the CaCl type and mixed CaMgCl typeand in anion triangle field samples were inthe SO4 and Cl which leads aciditycharacters to the ground water. The plot andchemical analysis shows that an alkalineearth mineral of Ca and Mg exceeds thealkali minerals of Na and K and strong acids

Cl and SO4 exceeds week acids HCO3.These various water types are generally aresult of hydro geochemical processoccuring in the subsurface system.

A simple and all can quickly understandingand viewed spatially about water quality intheir own region by computing water qualityindex with integration of GIS. Water qualityindex was computed according to the abovegiven equation (Eq-1 to Eq-4) with use ofBureau of Indian standards (BIS) for thepurpose of drinking water supplyrequirements. Here, 34 samples werecollected in and around Malattar sub-watershed and analyzed for the essentialparameters presented in table 2. Accordingto Water Quality Index classif ication,drinking water quality ranges from very poorto excellent if values ranges from 0 – 25and 90 – 100 respectively. If WQI < 100 it isfit for human consumption and is unfit fordrinking without treatment if its WQI > 100.Moreover, the larger the value of WQI, themore polluted the water concerned. FromTable 2, WQI ranges from a minimum valueof 27.66 to maximum of 136.21 with anaverage of 87.92. First, this implies that theground water of Malattar sub-watershed,Gudiyattam block needs some treatment tomake it fit for human consumption. Secondlysamples from well no 1 to 15 representspoor water quality and from 16 to 34represents the need for treatment beforeconsumption. Average value of WQI for thewhole region is 87.92 implies that the qualityof ground water is good. A better spatialview from local to global either in 2D or 3Dand know the quality of ground waterimmediately for the any area can beidentified easily by interpolation method withthe help of GIS as shown in figure5. Fromthe interpolation map, the ground waterquality shows more or less good in villagesnamed Redimakuppam(23), M.V. Kuppam(24), Chokarasohikuppam(25), Melpatti(17), Valathur(20), Kilpatti(22) andPangarisihuppam (19). The western part ofthe area shows medium to poor/fair qualityof ground water and treatments necessarilyrequires in the eastern part of area. Aninterpolated convex curve trend in the

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Eastern part indicates good quality of theground water and in the NW side indicatesmoderate to good quality. However thesetwo demarked location falls nearby alluvialplains and l ineaments density wheremoderately high with intersected to eachothers. It clearly indicates that the groundwater quality is controlled by geologicalstructures and land use practices. Theresult of the interpolation map wascorrelated with current satell ite data.Evidences proved that the center and NWpart agricultural land has developed well inthese two areas and water is in good quality.As a futuristic assessment ground water inthese areas need to be protected andconserved it from the other influences andalso points out the need for constructingthe check dams wherever possible.

CONCLUSION

The study which addresses the simplemathematical method to compute andunderstand the water quality in the studyregions which is applicable to allover thearea but its purely depends on the numberof chemical parameters is involved andavailability of data’s for the certain periods.Moreover, it is reliable method and caneasily predict the quality of the ground waterat present status. In this study 34 sampleswere collected according to the importanceof geological, geomorphological, land use/ land cover and WQI analyzed results thatthe minimum value of 27.66 to maximum of136.21 of poor to excellent respectively withan average value of 87.92. Besides wellnumber 1 to 15 represents poor waterquality and well number from 16 to 34represents the need for treatment. From thestudy, results were concluded that land useis mainly controlled the ground water qualityand also other factors such as rockdominance, geological structures, soilcover, runoff by stream surface flow andothers. Thus the drinking water in this areaare need to be treated with reverse osmosisprocess or ion exchange process beforesupplying human consumption as well asproperly manage and maintain the atpresent water bodies.

REFERENCES

Brown Robert, M., McClelland Nina, I.,Deininger Rolf, A. and Tozer Ronald, G.,1970. A water quality index - do we dare?Water and Sewage Works. October. pp: 339-343.

Craig, E. and Anderson, M.P., 1979. Theeffects of urbanization of ground waterQuality, A case study of Ground water, Vol.17,pp: 456-562.

Gibbs, R.J., 1970. Mechanisms controllingworld’s water chemistry, Science 170,1088-1090.

Gupta, Ravi P. 1991. Remote sensingGeology, Springer-Verlag Berlin Heidelburg,New york.

Herman Bouwer, 1978. Ground waterquality; Ground water hydrology, McGraw-Hill Kogakusha Ltd., 339-375.

Miller, G.T., 1979. Living in the Environment,Wadsworth Publishing Company,Belmond, California, pp.470.

Purandara, B.K. and Varadarajan, N., 2003.Impacts on groundwater quality byurbanization, J. Indian Water ResourcesSoc.23, 107.

Shankar and Latha Sanjeev, 2008.Assessment of Water Quality Index for theGroundwaters of an Industrial Area InBangalore, India. EnvironmentalEngineering Science, Volume 25, Number6, 2008 © Mary Ann Liebert, Inc.

Skidmore, A.K., Witske Bijer, Karin Schmidt,Lalit Kumar, K., 1997. Use of Remotesensing and GIS for sustainable landmanagement. ITC Journal. 1997, 3 (4), 302-315.

Tiwari, T.N. and Nayak, S., 2002. WaterQuality Index for the Groundwater ofSambalpur Town. In D. Prasad Tripathy andB. Bhushan Dhar, Eds. EnvironmentalPollution Research. New Delhi: A.P.H Pub.Corp., pp. 971.

291ECO-CHRONICLE

VEGETATION MAPPING OF CHIMMONY WILDLIFE SANCTUARY

Menon, A.R.R. and Suraj, M.A.*

Kerala Forest Research Institute, Peechi, Thrissur, Kerala.*Department of Botany, Sree Narayana College, Alathur, Palakkad, Kerala.

ABSTRACT

Large scaled forest cover maps are essential for the management of the Sanctuaries. Vegetationmap of Chimmony Wildlife Sanctuary was prepared using 1:15,000 Black& White Aerial Photographs.Standard photo interpretation techniques were adopted for the land cover classification and mapping.The final mapping scale is of 1:25,000 aiming to the forest managers for better sanctuarymanagement. Overall mapping accuracy was found to be 89%.

Key Words: Aerial photo-interpretation, Vegetation mapping.

INTRODUCTION

Management of forest resources of ourcountry is carried out through forest maps,which are also called as Working PlanMaps/ Stock Maps/ Management Plan Maps,etc., and are prepared at 10-15 yearsinterval. Forest maps are prepared to givebasic information about the forest types,blanks, cultivation patches, plantations, etc.At present the information needed for theworking plan is conveniently obtainedthrough ground-based surveys, which arebeset with inherent limitations. There isconsiderable t ime lag between theirpreparation and utilization. However, withthe advent of remote sensing data productsl ike aerial photographs and satell i teimageries, rapid progress is possible in themap production and the maps producedare also accurate and detailed (Doyle,1973). When monitoring forest areas,ground based investigations tend to belabor intensive, slow, expensive and datacollection in remote and inaccessible areasmay be almost impossible. RemoteSensing in combination with ground-basedstudies have proved to be an effective tool

in vegetation mapping and monitoring thechanges in natural resources (Deekshitaluand George Joseph, 1991). Remotelysensed information can be gathered fromairborne platforms such as, airplanes orspace bound platforms such as satellites.A picture/ imagery taken from air or spacecontain more information of a larger areathan the picture taken from the ground(Howard and Lanly, 1975). It incorporatestwo major sub fields; data acquisition fromsensor systems such as, cameras ormultispectral scanner and data analysis byqualitative methods (eg: photo-interpretation) or quantitative methods (eg:computer based decision making).

The structural information and land covermaps are essential for the management ofWildlife sanctuaries, National Parks andBiosphere reserves. In the present study,an attempt is made to map ChimmonyWildlife Sanctuary using 1:15,000 scaledAerial photographs.

Study area

Chimmony Wildlife Sanctuary is located


ECO-CHRONICLE292

between 10o 24’ to 10o 29’ N Lat and 76o

25’ to 76o 30’ E Long. and is situated in theMukundapuram taluk, Chalakudy forestdivision of Thrissur district of Kerala state.

METHODOLOGY

Aerial photographs were procured fromNational Remote Sensing Agency (NRSA),Hyderabad.

The aerial photographs procured fromNRSA were checked for flight defectsprinting or development defects, side andlateral overlaps, gap if any and scale,photographs of requisite quality only wereaccepted. Reconnaissance survey wasconducted with aerial photographs andmaps. During the field trips, groundfeatures were correlated with photographicimages with the help of pocket stereoscope(Sabins, 1978).

On the basis of information collected duringreconnaissance, a photo interpretation keywas prepared (Tomar, 1968), and was usedfor delineation of land cover classes andmapping. Survey of India Toposheets 58 B/7 and 58 B/11 of 1:50,000 scale; 58 B/7 NEof 1: 25,000 scale, working plan ofChalakudi Forest Division andManagement plan of Chimmony WildlifeSanctuary were used as ancillary data forthe present study. The aerial photographswere visually interpreted using the photo-interpretation key. After the completion ofphoto-interpretation, the interpreted detailswere transferred to base maps using opticalpantograph. Scale and tilt corrections weremade, wherever necessary. A base mapwas prepared from Survey of Indiatoposheets. The area estimation of differentland cover classes in the aerial photomapwas carried out using planex - 5000 digitalplanimeter. GIS and GPS technology wereused for mapping and accuracy evaluation.

OBSERVATIONAND RESULTS

Thirteen land cover classes were identifiedin the maps prepared from Black and White

aerial photographs. Three density classeswere identified for Evergreen forests (E1-5-20% density; E2-21-40%; E3->41%) andmoist-deciduous forest (MD1-5-20%density; MD2-21-40%; MD3->41%)respectively. Man made forests include teakplantation and mixed teak plantation. In themixed plantations, in addition to Teak,Bombax malabaricum trees were alsoobserved. In addition to these, two scrubclasses (open and dense), grass lands androcky area were identified (Fig: 1).

DISCUSSION AND CONCLUSION

The vegetation map prepared by remotesensing, GIS and GPS techniques was thefirst of its kind for the Chimmony WildlifeSanctuary. As such, it is a pioneering effortto map the vegetation in a systematicmanner.

In the map prepared from 1:15,000 aerialphotographs, 13 land cover classes wereidentified. Moist-deciduous and Evergreenforests (Champian and Seth, 1968) werefurther sub divided in to 3 density classesbased on canopy density. The total areacovered by the sanctuary is 85.067sq.km.and the total forested area in the sanctuaryis 66.24 sq.km.

REFERENCES

Champion, H.G. and Seth, S.K., 1968 .Revised Survey of Forest Types of India,Govt, of India Publications.

Deekshitalu, B.L.and George Joseph, 1991.Science of Remote Sensing. CurrentScience, 61 (3& 4): P. 129-135.

Doyle, F.J., 1973.Can satellite photographycontributes to topographic mapping. In Holz,K.R (Ed .). Remote Sensing of theEnvironment - The Survellit Saw Houghton,Boston, U.S.A.

Howard, J.A. and Lanly, J.P., 1975. Remotesensing for tropical forest surveys.Unasylva, 27(2): P. 32-37.

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No Covertype Tone Texture Pattern Remarks

1. Evergreen forest Black Fine Smooth -2. Semi evergreen forest Black Medium Smooth -3. Moist deciduous forest Dark grey Medium Coarse -4. Scrub open Grayish white Medium Medium -5. Scrub dense Dark grey Medium Coarse -6. Sub tropical hill forest Deep black Fine Smooth -7. Grass land White Fine Smooth -8. Regeneration Light grey Fine Smooth Distinct

crown shape9. Teak plantations Light grey Medium Coarse -10. Tribal settlements Yellow white Coarse - Distinct

appearance11. Exposed rock Light grey Rough - -12. Water body Dark black Fine - -

Table 1. Interpretation key for land cover mapping using pan-chromatic aerial photographs

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Sabins, F.F. 1978. Remote SensingPrinciples and interpretation. Freeman& Co.,San Francisco. 426p.

Tomar, M.S. 1968. Manual of photo-

interpretation in Tropical Forests (SouthernZone, Kerala and Madras) UNSF/GOI/FAO/PROJECT/IND/100/4. PreinvestmentSurvey of Forest Resources.

295ECO-CHRONICLE

ASSESSEMENT OF GROUNDWATER QUANTITY IN NOYYAL RIVER BASINUSING GIS

Brema, J. and Prince Arulraj, G.

School of Civil Engineering, Karunya University, Karunya Nagar,Coimbatore, Tamil Nadu.

ABSTRACT

Noyyal river basin is a water starved basin with reference to surface water. The main occupationof people associated with the basin is agriculture. As the surface water system is poorly maintained,groundwater is utilized for irrigation and domestic purposes. Hence it is imperative to study thepresent scenario of groundwater in the basin to avoid over exploitation.

The objective of this study is to assess the groundwater potential in Noyyal river basin using a GISbased model. Ten years of water level data covering 21 blocks of the basin have been used for thestudy. The estimated groundwater quantity has been correlated with the groundwater level andrainfall data. Attribute database was integrated with spatial location map using ArcGis 9.1 andmaps showing the spatial distribution of water quantity were prepared. Using these maps, thequantity of water available in the aquifer has been estimated using the changes in the water levelbefore and after monsoon, thickness of various layers in the aquifer and the correspondingspecific yield values. Correlation analysis has been carried out and it was found that that there isgood correlation between the groundwater recharge, rainfall and waterlevel.

Keywords: Water level, groundwater quantity, recharge, premonsoon, postmonsoon.

INTRODUCTION

Groundwater is one of the most importantnatural resource required for domestic,agricultural and industrial purposes. Theresource can be optimally used only if itsquantity is assessed with reasonabledegree of accuracy. Over the years,increasing population, industrialization,urbanization and expansion in agricultureetc. have resulted in unscientific exploitationof groundwater resulting in water crisis. Overexploitation of groundwater leads todeterioration of groundwater quality, whichmakes the water unfit for domestic, irrigationand industrial purposes. It has beenobserved that lack of simple methods ofgroundwater estimation has lead tomishandling of the resource. Thusassessment of groundwater is essential tomaintain a proper balance between itsquantity and usage. This article presents amethod for estimation of groundwater

quantity using Geographic InformationSystem (GIS).

Literature review

Many earlier attempts were carried out inthis direction. Ashok Kumar et al., (1999)analyzed the fluctuations in the water levelusing Digital Basement Terrain Model(DBTM). The study indicated that remotelysensed l ineaments are important forgroundwater exploration and it can beascertained using DBTM. Ashok Kumar andSavita Tomar (1998) assessed thegroundwater resources usingHydrogeomorphological and Geophysicalsurveys. Hydrogeomorphological unitswere delineated using images and the layerresistivity was analyzed using electricalresistivity method. Narasimha Prasad(2003) carried out a study on pattern ofwater level fluctuations, hydro geologicalproperties of the rocks and groundwater


ISSN:0973-4155

ECO-CHRONICLE296assessment using the parameters such asdrainage, base flow and rainfall. Jayakumarand Ramasamy., (1996) made an attemptto understand the geology andgeomorphology, which control theoccurrence of groundwater in Attur valley andconcluded that deformational features havea major control over groundwatermovement. Palanivel and Ramasamy(2000) carried out a study to demonstratethe concept of hardrock aquifer modelingusing permeability, storage co-efficient,waterlevel data and lithology details, usingoverlaying technique. Palanivel andRamasamy (2002) carried out a study toevaluate the relationship between the foldedstructures and the groundwater movementin a test site south of Cauvery andconcluded that folded structures haveappreciable control over groundwater flow.

Study Area

Noyyal river is a tributary to the river Cauveryand the basin is situated in the western partof the state. The basin has an aerial extent

of 3550 sq.km. The basin lies between thelatitudes 10o53’1.06" N to 11o21’57"N andlongitudes 76o37’49"E to 78o12’55.06"E.Thebasin has a length of about 175 km fromwest to east with an average width of 25km. The River basin consists of three subbasins namely Vanathaangarai, UpperNoyyal and Lower Noyyal. The basin fallswithin Coimbatore, Erode and Karur districtsof Tamilnadu. The western periphery of thebasin is the western ghats which has anaverage altitude of 2200 meters (7220 feet)above mean sea level .The averageelevation of the terrain of the basin in thewestern part near the foot of the hills is about450 meters and it slopes towards east .Theaverage gradient of the basin is about 2.5meters per kilo meter(1 in 400). The soiltype in Noyyal basin varies from shallow rednon calcareous soils to very deep greycalcareous ones. The rainfall in the basinis influenced by north-east monsoon andalso by pre-monsoon showers and south-west monsoon. The flow in the system isactive only during the monsoon periods andduring the rest of the period, it is found to be

Fig.1. Noyyal River Basin

Fig.2. Location of observation wells

297ECO-CHRONICLE

8

11 jijij

n

ii Sla

dry. There are 18 raingauge stations in andaround the basin. The average annualrainfall in the basin is 714 mm. The studyarea is shown in Fig.1.


Rainfall and ground water quantity analysiswas carried out to find the relationshipbetween rainfall and recharge in the studyarea, over a period of ten years spanningbetween 1995 to 2005. Pre and postmonsoon groundwater table depth wasanalyzed to study the changes ingroundwater level over the period. Waterlevel data from 30 locations have been usedfor the study. Water level data have beencollected from State Ground and SurfaceWater Resources data centre, Chennai. Thelocations of the observation wells areshown in Fig.2.

Water level data before and after monsoonhas been assigned in the respective spatiallocations using the software ArcGis 9.0 andWater level contour maps were preparedusing the spatial analyst tool of ArcGis 9.0software. The lithology details of the aquifercollected from the borehole data, by theSurface and Groundwater Resources DataCentre, Tharamani, Chennai were used forthe study. The thickness of various layers ofthe aquifer such as topsoil, valleyfil l,weathered zone etc., were given as attributedata at various locations throughout theaquifer. The specific yield values for variouslayers shown in Table.1 were also includedin the attribute table. Using the water tablelevel data, the depth of water in the various

layers was calculated. For the purpose ofestimation of groundwater, the entire basinwas divided into cells of size 300m x 300m.Each cell was assumed to consist of eightvertical layers mentioned in Table 1. If anylayer is not available in the cell, thethickness of that layer is given as zero.

The groundwater stored in each cell of theaquifer was calculated by multiplying thedepth of water in each layer and the specificyield of each layer. Thus the groundwaterstored in the basin was calculated usingthe formula given below:

GW = …….(1)

Where, lij is the jth layer of the ith cell in theaquifer and Sij is the specific yield value forthe corresponding cell in the layer, a i is thearea of the ith cell in m2 and GW is the totalgroundwater available in the aquifer in Mm3.The quantity of groundwater available in theaquifer was estimated before and after theSouthwest and Northeast monsoons.

The annual groundwater potential of thebasin has been found from:GWP = Water available in the basin aftermonsoon – Water available in the basinbefore monsoon + Water utilized during theperiod.


Quantity of Groundwater

The Water levels below the ground levelobserved during the months of April to Julyand August to December were consideredduring the analysis. These monthscorrespond to the period of Southwest andNortheast monsoons respectively in theregion. A raster map of the quantity ofgroundwater available in each cell has beenprepared using ArcGis 9.0 software basedon the groundwater table level, thickness ofvarious layers in the aquifer and specificyield values and the map is shown in Fig.3.

Using the map, the total groundwateravailable in the basin during a particularperiod can be calculated using equation (1).Table 2 shows the groundwater potentialestimated using the model during theNortheast and Southwest monsoon.

Sl.No.

Aquifer layer SpecificYield

1. Top soil 0.0682. Valley fill 0.000153. Highly weathered

zone0.042

4. Weathered zone 0.0325. Partially weathered

zone0.03

6. Fractured zone 0.037. Jointed rock 0.0258. Partially Jointed rock 0.002

Table 1. Values of Specific Yield

ECO-CHRONICLE298The groundwater recharges in the basinduring both the monsoon periods havebeen calculated from the premonsoon andpostmonsoon groundwater potential andgroundwater utilized for irrigation. Theirrigation water requirement for the wholebasin has been calculated using the detailssuch as crop water requirement, croppingpattern and crop seasons. The waterrequirement for irrigation for a period of oneyear was found to be 902 Mm3. The irrigationwater requirement has been distributed as35% during the southwest monsoon and65% during northeast monsoon. Thepercentage of distribution is taken as 65%during northeast monsoon as the timeduration between northeast andconsecutive southwest monsoon is

Year Northeast monsoon Southwest monsoon

Premonsoon Postmonsoon Premonsoon Postmonsoon

1995 4778 5067 4587 50541996 4586 4847 4669 50051997 4964 4836 4873 48921998 4777 4937 4587 48351999 4852 4707 4544 50672000 4764 4723 4645 45832001 4414 4339 4464 46632002 4559 4357 4253 44322003 4609 4505 4516 44632004 4405 4893 4369 44742005 4778 4976 4587 5020

Table 2. Groundwater Available in Mm3

comparatively more. The values of theestimated groundwater recharge and otherdetails are given in Table 3.

It is inferred from the table that the rechargeduring the southwest monsoon was foundto be least for the year 2000 and the rainfallwas also found to be the least. It can beseen from Table.3 that the recharge duringthe northeast monsoon was the least forthe year 2002 since the rainfall value duringthe northeast monsoon is the least for thatyear.

Groundwater level fluctuation

The fluctuations of the groundwater table ofthe study area before and after monsoon

Fig.3. Groundwater Quantity in the Aquifer

299ECO-CHRONICLEwere analyzed using the data available forthe 30 locations over the study area. Theaverage water level in the basin wascalculated from these data. Fig.4. showsthe variation in water table level before andafter North-East monsoon during the studyperiod. It is observed that the maximumincrease in water table level was found tobe 3.16m during the year 2004. The averageincrease in the water level of the basinranges from 3.16m to 0.7m.

Fig.5. shows the variation in water tablelevel before and after Southwest monsoonduring the study period. It is observed thatthere is a considerable increase in watertable level of 2.27m during the year 1995.The basin’s average increase in the waterlevel fluctuations range from 2.27m to 1.0m.

The variation in the Groundwater rechargecorresponding to water table fluctuation andaverage rainfall during northeast monsoonseason is shown in Fig.6. The graph showsthat with increase in water table fluctuationand rainfal l , there is increase ingroundwater recharge also. The correlation

Cumulative Rainfall inmm

Water Tablefluctuation in m

Recharge in Mm3

Year

Southwestmonsoon

Northeastmonsoon

Southwestmonsoon

Northeastmonsoon

Southwestmonsoon

Northeastmonsoon

TotalRecharge

1995 568.8 458.3 2.18 2.13 782.7 875.3 1658

1996 484.9 448.6 1.95 2.19 651.7 847.3 1499

1997 234.3 259.7 1.17 1.40 334.7 458.3 793

1998 297.8 438.9 1.91 2.00 563.7 746.3 1310

1999 450.0 271.3 2.27 1.36 838.7 441.3 1280

2000 192.4 287.6 1.23 1.60 253.7 545.3 799

2001 281.9 289.9 1.00 1.48 514.7 511.3 1026

2002 266.4 122.6 1.69 0.70 494.7 384.3 879

2003 228.7 263.6 1.21 1.48 262.7 482.3 745

2004 212.5 421.2 1.42 3.16 420.7 1074.3 1495

2005 251.4 469.2 2.11 2.15 748.7 784.3 1533

Table.3. Estimated values of groundwater recharge in Mm3

between groundwater table level fluctuationand groundwater recharge was found to be0.956. The correlation coefficient betweengroundwater recharge and cumulativeaverage rainfall of the basin during thenortheast monsoon period was found to be 0.87.

The variation in the Groundwater rechargecorresponding to water table fluctuation andaverage rainfall during southwest monsoonseason is shown in Fig.7. The correlationcoefficient between groundwater tablefluctuation and groundwater recharge wasfound to be 0.88. The correlation coefficientbetween groundwater recharge andcumulative average rainfall during themonsoon period was found to be 0.899.

CONCLUSION

A GIS based methodology has beendeveloped for the estimation of groundwaterpotential in Noyyal river basin aquifer. Sincethe spatial variation of rainfall and the spatialvariation of the aquifer have beenconsidered for the estimation ofgroundwater potential, the results of the

ECO-CHRONICLE300model can be considered rel iable.Correlation between the rainfall and thegroundwater potential, as well as thecorrelation between the water table and thegroundwater potential have been found. Thecorrelation between the rainfall, water table

and groundwater recharge is found to begood.

REFERENCES

Ashok Kumar, Savita Tomar and Lal BihariPrasad,1999. Analysis ofFractures Inferred from DBTM andRemotely Sensed data forGroundwater Development inGodavari Sub-Watershed, Giridh,Bihar, Journal of Indian Society ofRemote Sensing, Vol.27 No.2,pp:105 -114.

Ashok kumar and SavitaTomar,1998. GroundwaterAssessment through hydrogeomorphological and Geophysicalsurvey – A case study in GodavariSub-Watershed, Giridh, Bihar,Journal of Indian Society of RemoteSensing, Vol. 26 No.4, pp: 177 -183.

Narasimha Prasad, N.B., 2003.Assessment of GroundwaterResource in Nileshwar RiverBasin, Journal of Applied Hydrology,Vol. XVI No.3, pp: 52 – 60.

Jayakumar, R. and Ramasamy,S.M., 1996. Groundwater targetingin hard rock terrains throughgeomorphic mapping: A case study,Asian Pacific Remote Sensing andGIS Journal, Vol. 8, No. 2, pp:17-23.

Palanivel, K. and Ramasamy, S. M.,2002. Functions of Groundwaterflow in folded aquifer systems,Western ghats region: usingremote sensing and GIS,Proceedings of “IT enabled Spatialdata Services”, BharthidasanUniversity, Tiruchirapalli, pp:205-207.

Palanivel, K. and Ramasamy, S.M.,2000. GIS and hardrock aquiferfunction modeling in western ghats,Proceedings of “Geomatics 2000”,P.S.G College of Technology,Coimbatore, pp: 221-225.

-20

-15

-10

-5

0

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

-20

-15

-10

-5

0

waterlevel aftermonsoon

waterlevel beforemonsoon

Fig. 4.Groundwaterlevel variationbefore and afterN-E monsoon

-20

-15

-10

-5

0

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

waterlevel beforemonsoon

Waterlevel aftermonsoon

Fig. 5.Groundwaterlevel variationbefore andafter S-Wmonsoon

Fig. 6. Groundwater Table Fluctuation, average monsoonrainfall and groundwater recharge during N - E monsoon

Fig. 7. Groundwater Table Fluctuation, average monsoonrainfall and groundwater recharge during S - W monsoon

301ECO-CHRONICLE


ISSN:0973-4155

FINANCIAL PERFORMANCE OF WOMEN MICRO ENTERPRISES INTHE ERA OF GLOBALISATION

Ramanunny, M. and Lalitha, N.

Department of Rural Development, Gandhigram Rural University, Dindigul,Tamilnadu.

ABSTRACT

The last one decade witnessed substantial growth in the number of women micro enterprises allthroughout the country. The chances offered by globalization were one of the reasons for creatingnew opportunities in the market for the entrepreneurs to venture in. But globalization also posedchallenges to the micro enterprises, especially those run by women. Infusion of technology, backingof marketing infrastructure etc. are the major strengths of global players which are causing seriousthreat to the existence of micro enterprises. A study was conducted in Thrissur District, Kerala, toanalyze the performance of women micro enterprises during the last four years to assess theimpact of globalization.

Key words: Financial viability, Kudumbashree, Women Micro Enterprises.

BACKGROUND

Globalization is the process of integrationof all the economies of the world so thatthere is free movement of goods, services,technology and capital, including labour,across national boundaries. Globalizationis said to have contributed heavily to bringabout vast changes in the lives of peopleespecially women. As part of these changes,women who are ready to make use of newopportunities have entered the traditionalmale bastions of entrepreneurship. It isreckoned that those who are averse tochange will suffer badly because of theshifting nature of agriculture, increasingcompetition in industry and the shrinking oftraditional employment avenues. However,with the enterprises started by women,worries remain as to whether theseenterprises wil l be able to face thechallenges posed by technologicalchanges and stiff competition, especiallyfrom global players. It is possible that manyof the new micro enterprises might perishdue to the competition. Hence this study

was done to evaluate the financial viabilityof micro enterprises run by women in theera of globalization.


The study was conducted with the objectiveof assessing the financial viability of micro-enterprises run by women in ThrissurDistrict of Kerala State. Thrissur District waschosen because of the centrality of locationand presence of maximum number ofwomen micro enterprises registered withDistrict Industries Centre andKudumbashree.

Sample

The study design is descriptive. Multi stagestratified random sampling is used forselection of sample. The micro enterprisesare categorized into urban, rural, individual,traditional, innovative etc. Samples weredrawn from District Industries Centre,Poverty Eradication cell of District Panchayatand State Poverty Eradication Mission

ECO-CHRONICLE302

(Kudumbashree). This comprised ofwomen entrepreneurs involved inproduction, manufacturing, trading andservicing type of enterprises. From apopulation of 2342 enterprises run bywomen, those having existence of 3 or moreyears were included. There were 810 suchenterprises which were divided into 16 subgroups. Using random number tables, 127were selected which formed the populationfor this study. The details are shown in Table 1.

Of the 127 units, 8 were excluded. Coconutpalm climbing is attempted mainly by men.Even though subsidy was paid to thewomen entrepreneur, the unit was run byher spouse. After their marriages, 2entrepreneurs migrated to a nearby districtleading to the closure of the units. Twoentrepreneurs registered both in DIC andKudumbashree which resulted induplication of data. One of the units startedafter registration became defunct and oneunit has not started. The details showingthe list of enterprises which were excludedare shown in Table 2.

Thus a total of 119 units, of which 75manufacturing, 1 assembling, 6processing, 7 job work, 3 repair andmaintenance and 27 service enterprises –were contacted for the purpose ofinvestigation.

Data collection

The entrepreneurs selected for the studyconstitute the primary source of data.Information about the working of the unit,reason for the starting of unit, source offinance etc constitute primary data. Theinformation recorded in the books andregisters of the unit and recorded by thevarious promotional agencies such as DIC,District Panchayat etc along with theinstructions issued by government andbanks constitute secondary data. For datacollection, 2 different types of interviewschedules, one for individual and the otherfor group enterprises were prepared. Theschedules were pre-tested and difficultitems were removed leading to improved

field functionality. To identify emergingbusiness opportunities, participatory ruralappraisal is conducted with entrepreneurs,representatives of NGOs and the linedepartment functionaries.

After finalizing the research design andinterview schedule, data were collectedthrough personal interviews with theentrepreneurs. Prior to data collection allrelevant secondary information werecollected with regard to the number ofagencies, institutions working for womenentrepreneurs, type of assistance renderedby various agencies and the circulars andguidelines issued by Ministry of Industries,Government of India, State Directorate ofIndustries, Department of Local SelfGovernment Institutions, Kudumbashree,NABARD and RBI.

Data analysis

The responses for each item in the interviewschedule were scored and recordedelectronically. Statistical analysis was doneusing Mstat C, a software . The raw scoreswere converted into indices. The statisticaltests done included frequency, percentage,analysis of variance, correlation coefficientsand multiple regressions. Case studieswere also prepared to record the progressor success of the entrepreneurs.

RESULTS

Only 33% of units studied were found to beprofitable and 67% of units had incurredlosses. Due to loss, 12 % of units had beenshut down and 55% of units were stillfunctional (Table-3A & B).

Over 4 years, the number of profitable unitshad increased from 11% to 33% (Table-4).Of these, 17% were manufacturing units,8.4% were service sector units, 4.2% jobworks, 1.6% processing, 0.8 % assemblingand 0.8% repair and maintenance unit.

Of the loss making units, it was found that69% of units were in the manufacturingsector. However, of the profit making units,only 51% of units were in the manufacturingsector.

303ECO-CHRONICLE

It was found that 92% of enterprises were sellingtheir products in the local market and only 4.2% ofunits were exploiting the potential outside the localmarket. The dependence of 2.5% of units ongovernment departmental purchases was regardedas a major threat for their existence (Table-6).

Sl.No.

Category Total

1 Rural Individual Traditional Product 382 Rural Individual Traditional Service 143 Rural Group Traditional Product 44 Rural Group Traditional Service 15 Rural Individual Innovative Product 216 Rural Individual Innovative Service 87 Rural Group Innovative Product 28 Rural Group Innovative Service 29 Urban Individual Traditional Product 810 Urban Individual Traditional Service 311 Urban Group Traditional Product 612 Urban Group Traditional Service 413 Urban Individual Innovative Product 514 Urban Individual Innovative Service 415 Urban Group Innovative Product 116 Urban Group Innovative Service 6

Total 127

Table 1. Categorization of entreprises selected forstudy.

Sl.No.

Reason No.ofUnits

1No. of units ceased working due tomarriage of entrepreneur 2

2No. of units started but not workingnow 1

3No. of units registered but not startedworking 1

4 No. of units run by men 15 No. of units that could not be found out 1

6No. of units selected because ofduplication of data 2

Table 2. List of enterprises whose details are notavailable

About 80% of the units were selfemployment units and less than20% are utilizing either family orhired labour.

Bank loans constituted the majorsource of capital fol lowed bygovernment subsidies andrevolving fund from localgovernments. Only in less than 5%of cases did a second infusion offunds happen.

As mentioned earlier, 67% of unitshad not reached a break evenstage. Even though 55% of unitswere incurring losses, theirpromoters/entrepreneurs werestill receiving their salaries. Inother words, they were justmanaging the businesses withoutmaking a profit but were offeringemployment to the entrepreneurs.

The majority of the respondentsshowed signs of empowerment.The selection of activity,management and confl ictresolution helped them to improvetheir decision making skills. Mostof them are of the opinion thatwomen should concentrate onbusiness matters and contributeto the family income along withmen.

DISCUSSION

This study looked at the impact ofglobalization on the success orotherwise of women’s microenterprises in Kerala. The microenterprises were spread across avariety of markets including someniche markets. Most of thesuccessful ones were those whichmanaged to find themselves inniche sectors. The discovery ofsuch markets depended on theeducational qualifications, familybackgrounds and socio economicprofi les of the entrepreneurs.

ECO-CHRONICLE304

Therefore, it has to be concluded thatentrepreneurship remains a difficult areato train people in. As far as women’sentrepreneurship is concerned, there is alack of good models to guide them.

When successful enterprises are studied itis seen that they have developed enterprise-specific strategies rather than followed ageneral model leading to difficulties ingeneralizing from successful models.

Against the background of globalization, therealit ies of the marketplace are thatconsumer needs are often created whichare then met through businesses. Often, bythe time others recognize the sameopportunities, the markets would have

Proprietary Partnership Co-operative

InformalAssociation/ SHG /NHG

Total

a Manufacturing 15 4 - 1 20

b Assembling - - - 1 1

c Processing - - 1 1 2

d Job Work 5 - - - 5

e Repair &Maintenance 1 - - - 1

f Service 6 - - 4 10

Total 27 4 1 7 39

Table 3. A. Classification of profit making entreprises

Proprietary Partnership Co-operative

InformalAssociation/ SHG /NHG

Total

a Manufacturing 35 3 1 7 46

b Processing 3 - - 1 4

d Job Work 1 - 1 - 2

c Repair &Maintenance

1 - 1 - 2

d Service 8 - - 4 12

Total 48 3 3 12 66

Table 3. B. Classification of entreprises working on loss

become saturated. Women’s enterprisesstart off with many weaknesses and acombination of strategies might have to beused in order to taste success.

One of the findings in this study was thatnearly 70% of the loss-making units werein the manufacturing sector. However, only50 % of the profit-making units were in thatsector. This suggests that close attentionneeds to be paid to various aspects of thissector which decide the profitability of microenterprises. For sales to increase, the needis to tap into markets beyond the local ones.For this to happen, micro enterprises haveto compete with far bigger players and alsowith the micro units of that area. Thisrequires infusion of capital as well as

305ECO-CHRONICLE

YearSl.No

Name of Unit

2004-05

2005-06

2006-07

2007-08

1 Aiswarya Herbal Beauty Parlour Profit Profit2 Hexogon Corrugated Profit Profit Profit Profit3 Kannampuzha Engineering Works Profit Profit Profit Profit4 Kerashree Profit Loss Profit5 Krishna Printers Profit Profit Profit Profit6 M.M.V Product Profit Profit Profit7 Mahalakshmi Garments Profit Profit Profit Profit8 Neighbours Profit Profit Profit Profit9 New Sathya Garments Profit Profit Profit Profit10 Nishinas Collections Profit11 St.Thomas Mosquito Umbrella Profit Profit12 Swagat Forging Process Profit13 Vijayasree Catering Profit Profit Profit14 Bagyavathy Handlooms Profit15 Bhavana Jewellery Die Works Profit Profit Profit Profit16 Classic Brassiers Profit Profit Profit Profit17 Infinne Inner Garments Profit Profit18 Mails India Profit19 Parakudiyil Hollow Bricks Profit20 PLY Max Profit21 Rubber Land Profit Profit Profit Profit22 Santhwanam Profit Profit Profit23 Shelfy Electronics Profit Profit Profit Profit24 Sirosauthi Natural Herbal Oil Profit25 Sneha Garments Profit26 Venu's Concrete Furniture Works Profit27 Winsome Apparels Profit Profit Profit Profit28 Champion Industries Profit Profit Profit29 Favourite Food Products Profit Profit Profit30 Gurukripa Profit Profit Loss Profit31 Nisari Industries Profit Profit Profit32 Shari Binu (Lakshmi Garments) Profit33 Sneha Deepam Food Products Profit34 Tasty Canteen & Catering Unit Profit35 Akshaya Centre Profit Profit36 Akshaya Tailoring Centre Profit37 Clean Kerala Loss Loss Loss Profit38 Lalitha Industries Profit Profit Profit Profit39 Thejas Garments & Tailors Profit Profit

Table 4. List of entreprises working on profit along with the year

ECO-CHRONICLE306

improvements in technology. However, wefound that less than 5 % of the units hadbrought in additional capital after the initialoutlay. Also, in most cases, the surplusgenerated was not ploughed back into thesame ventures.

Another f inding was that successfulenterprises led to the establishment ofsimilar but rival units leading to an increasein local competition.

CONCLUSION

It is clear that there is no single remedy thatcan sort out the problems faced by womenentrepreneurs. But a comprehensivestrategy involving all stake holders includingthe family members of the entrepreneurs isrequired to ensure the sustenance of suchenterprises. Society should see theproblems of entrepreneurs as their own andhelp to evolve strategies which arecomprehensive, holistic and futuristic.

REFERENCES

Barreto Humberto, 1960. The Entrepreneurin micro economic theory-Disappearanceand explanation- Routledge, London, NewYork.

Brochi, W.G. 1978. The village entrepreneur.Cambridge Harvard University Press.

Bygrave, W.D. and Hofer, C.F., 1991.

a Local Market 109

b Marketed Outside The State 4

c Export 2

d B-B Arrangements / Sold toMother Unit 1

e Depending on Government /Department Purchase 3

Total 119

Table 5. Classification of entreprises onthe basis of markets

Theorizing about entrepreneurship,Entrepreneurship theory and practice, 16(2),PP 13-22.

David, L. Birch, 1987. Job creation inAmerica. Free Press, New York.

Desai Vasant, 1991. EntrepreneurialDevelopment, Vol.1, Himalaya PublishingHouse, Mumbai.

Government of India, 2007. Report onconditions of work and promotion of livelyhoods in organized sector, NCEUS, NewDelhi.

Institute of Small Enterprise andDevelopment, 2008. India Micro, Small andMedium Enterprises – Report, ISED, Kochi.

Jan Tinbergen, 1967. Developmentplanning, translated from the Dutch by N.D.Smith, World University Library, London.

Jaya, S. Anand, 2002. Self Help Groups inempowering women, case study of selectedSHGs and NHGs, RainbowPublishers,Noida,U.P.

Lalitha, N. and Nagarajan, B.S., 2002. SelfHelp Groups in Rural Development-Dominant Publishers and Distributers, NewDelhi, Page 230.

Mathew, P.M., 2007. Social Enterprise underglobalization - International Institute ofcorporate social responsibility, Nottingham.

Mridul Eapen, 2003. “Rural Industrializationin Kerala: Re examining the issue of RuralGrowth Linkages”, International Seminarheld in January 2003, Dept. of AppliedEconomics, CUSAT Page 33-34.

Mukesh Gupta, 2006 . Theory ofentrepreneurship, Raj Publishing House,Jaipur.

Purushotham, P., 2006. National Study onSGSY: A process study centre for selfemployment and Rural Enterprises, NIRD.Hyderabad, Page 132.

307ECO-CHRONICLE

Rabindra, N. Kanungo (Ed), 1998.Entrepreneurship and innovation – Modelsfor Development – Sage Publications, NewDelhi.

Sreeramulu, G., 2006. Empowerment ofwomen through Self Help Group – KalpazPublication, New Delhi.

Swain, C.B. and Tucker, W.R., 1973. Theeffective entrepreneurs. General LearningPress, New Jersey.

Takshak, Renu., 1990. Credit procurementand utilization by women entrepreneurs.M.Sc. thesis submitted to HaryanaAgricultural University, Hisar.

Vineetha Menon, P.R., Gopinathan Nair, NairK.N., (Eds), 2005. Alleviating Poverty, Casestudies of Local Level Linkages andProcesses in the Developing World.Rainbow Publishers, Noida, UP.

ECO-CHRONICLE308

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