mapping of bio-waste resources across india

8/11/2019 Mapping of Bio-waste Resources Across India

1/66

Mapping of bioMapping of bio waste resources waste resources across Indiaacross India

CombustionCombustion, Gasification & Propulsion Laboratory, Gasification & Propulsion LaboratoryDepartment of Aerospace EngineeringDepartment of Aerospace Engineering

Indian Institute of Science, Bangalore 560 012Indian Institute of Science, Bangalore 560 012http://cgpl.iisc.ernet.inhttp://cgpl.iisc.ernet.in


2/66

The Biomass Assessment ProgrammeThe Biomass Assessment Programme

andand


3/66

The KeyThe KeyAspects of the Aspects of the Work Work

2.2. GraphicalGraphical vectorizationvectorization forfor thethe basebase GISGIS layerslayers..

.. ..

4.4. CurrentCurrent StrategyStrategy toto identifyidentify CropCrop isis byby ImpliedImplied NDVINDVI of of LandLanduseuse areaarea andand StatisticalStatistical CropCrop areaarea extentextent. .

5.5. NewNew StrategiesStrategies forfor CropCrop IdentificationIdentification useuse of of NDVINDVI andand

otherother parametersparameters withwith AIAI (artificial(artificial intelligenceintelligence) ) techniquestechniques..6.6. CreateCreate strategicstrategic queryquery responsesresponses forfor aa varietyvariety of of usersusers. .

7.7. ProvideProvide optionsoptions forfor dynamicdynamic queriesqueries withwith bothboth graphicalgraphical oror

..8.8. ResolveResolve TalukTaluk levellevel datadata spatiallyspatially


4/66

What is GIS?What is GIS?

thethe studystudy of of thethe EarthEarth andand itsits lands,lands, features,features,inhabitants,inhabitants, andand phenomenaphenomena. .

GISGIS isis thethe technologytechnology usedused toto automateautomate thethegeographicalgeographical datadata analysisanalysis byby makingmaking useuse of of computationalcomputational powerpower of of ComputerComputer. .

GISGIS containscontains thethe methodologiesmethodologies toto definedefine andandaccessaccess ee geograp cageograp ca spacespace. .


5/66

GISGIS softwaresoftware enablesenables GeographyGeography byby providingproviding aa

What objects of GIS represent Geography?What objects of GIS represent Geography?

GraphicalGraphical surfacesurface tunedtuned toto aa CoCoordinateordinate systemsystem TheThe geographicalgeographical informationinformation areare layeredlayered inin GISGIS

forfor MultiMulti FeatureFeature representationrepresentation.. ForFor ee..gg..StreetsStreets areare shownshown aboveabove thethe LandLand useuse layerlayer..

The Geographical features are The Geographical features are

Polygons in GIS Vector Layers. Polygons in GIS Vector Layers. For e.g. Points show Customer For e.g. Points show Customer

Address, Lines Show Streets and Address, Lines Show Streets and Polygons show Land use.Polygons show Land use.


6/66

What is Land use?What is Land use?

earthearth. . LandLand coverscovers includeinclude grass,grass, asphalt,asphalt, trees,trees, barebareground,ground, water,water, etcetc..

ThereThere areare twotwo primaryprimary methodsmethods forfor capturingcapturing informationinformationonon landland covercover:: fieldfield surveysurvey andand analysisanalysis of of remotelyremotelysensedsensed datadata [RSD][RSD]..

LandLand covercover isis distinctdistinct fromfrom landland useuse despitedespite thethe twotwo termstermsoftenoften beingbeing usedused interchangeablyinterchangeably. .

LandLand useuse isis aa descriptiondescription of of howhow peoplepeople utilizeutilize thethe landland(Urban(Urban andand agriculturalagricultural landland usesuses areare twotwo of of thethe mostmostcommonlycommonly recognisedrecognised highhigh levellevel classesclasses of of use)use).. ThereTheremaymay ee mu p emu p e anan a erna ea erna e anan usesuses sucsuc asas oresores

andand WasteWaste landslands forfor EnergyEnergy..


7/66

Land Use as seen by SatelliteLand Use as seen by Satellite

SpatialSpatial representationrepresentation of of LandLand useuse isis donedone byby SatelliteSatellite asas anan imageimageseenseen byby thethe IRIR andand VisibleVisible lightlight rangerange cameracamera. . AA samplesample forforKarnatakaKarnataka isis shownshown herehere::

TheThe ImageImage providesprovides anan indexindex forfor

VegetationVegetation. . ThisThis isis usedused toto groupgroup thetherespectiverespective similarsimilar PixelsPixels andand classifiedclassifiedintointo Corres ondinCorres ondin LandLand UseUse Pol onsPol onsCalledCalled VectorsVectors ..


8/66

Land Use as seen in GISLand Use as seen in GIS

S atialS atial re resentationre resentation of of LandLand useuse isis donedone inin GISGIS throu hthrou h irre ularirre ularPolygonsPolygons..

LULUPol onsPol ons aa ra hicra hic illustrationillustration: :


9/66

NDVINDVI == NormalizedNormalized differencedifference VegetationVegetation IndexIndex

What is NDVI?What is NDVI?

ItIt isis defineddefined asas NDVINDVI == (NIR(NIR VIS)VIS) // (NIR(NIR ++ VIS)VIS) wherewhere NIRNIR == NearNear infraredinfraredreflectionreflection andand VISVIS == VisibleVisible reflectionreflection. .


10/66

TheThe ideaidea isis thatthat healthhealth ve etationve etation absorbsabsorbs mostmost of of

What is NDVI? Contd...What is NDVI? Contd...

thethe visiblevisible lightlight thatthat hitshits it,it, andand reflectsreflects aa largelargeportionportion of of thethe nearnear infraredinfrared lightlight.. UnhealthyUnhealthy oror

nearnear infraredinfrared lightlight..

NDVINDVI forfor aa givengiven pixelpixel alwaysalways resultresult inin aa numbernumber thatthat

rangesranges fromfrom minusminus oneone ((11)) toto plusplus oneone (+(+11));;,, ..zerozero meansmeans nono vegetationvegetation andand closeclose toto ++11 ((00..88 00..99))indicatesindicates thethe highesthighest possiblepossible densitydensity of of greengreen leavesleaves..

HowHow toto identifyidentify crops?crops? ..


11/66

WhenWhen sunlightsunlight strikesstrikes objects,objects, certaincertain wavelengthswavelengths of of itsits

NDVI for Land and Crop ClassificationNDVI for Land and Crop Classification

spectrumspectrum areare absorbedabsorbed andand otherother wavelengthswavelengths arearereflectedreflected. . TheThe pigmentpigment inin plantplant leavesleaves calledcalled

,, ..00..77 m)m) forfor useuse inin photosynthesisphotosynthesis..

TheThe cellcell structurestructure of of thethe leaves,leaves, onon thethe otherother hand,hand,

stronglystrongly reflectsreflects nearnear infraredinfrared lightlight (from(from 00..77 toto 11..11mm ..

asease onon ee cons era oncons era on aa ee a sorp ona sorp on ananreflectancereflectance of of thethe visiblevisible lightlight andand IRIR willwill varyvary fromfrom cropcroptoto crocro thethe methodmethod isis ado tedado ted toto resolveresolve thethegeographicalgeographical cropcrop areaarea intointo differentdifferent cropscrops..


12/66

Reclassification of Reclassification of AgriAgriLand use (LU)Land use (LU) UsageUsage of of thethe samesame LULU DataData forfor subsequentsubsequent yearsyears (about(about 1010)),, asas

longlong asas thethe areaarea underunder thethe agriculturalagricultural activityactivity inin thethe selectedselectedzonezone remainsremains roughlyroughly samesame aa featurefeature generallygenerally truetrue. .

LanLan useuse asas eeneen c ass ec ass e asease onon NDVINDVI ana ys sana ys s oo t et e earteartsurfacesurface temporallytemporally i i..ee.. seasonseason wisewise .. LandLand useuse mapmap forfor eacheach statestateisis availableavailable atat TalukTaluk levellevel.. ItIt containscontains thethe agriculturalagricultural landland classclasspolygonspolygons basedbased onon seasonsseasons Kharif Kharif,, Rabi,Rabi, Kharif Kharif RabiRabi..

InIn thethe currentcurrent method,method, polygonspolygons areare classifiedclassified intointo specificspecific cropscropsonon thethe basisbasis thatthat samesame typetype of of cropcrop getget intointo thethe samesame polygonpolygonduedue toto ImpliedImplied NDVINDVI forfor landland useuse andand MajorMajor cropscrops gogo intointo largerlarger

AIAI isis usedused toto dodo thethe spatialspatial distributiondistribution of of CropsCrops intointo LandLand useuseol onsol ons usinusin Ma orMa or cro scro s forfor lar erlar er ol onsol ons StatisticalStatistical CroCro

areaarea andand otherother aprioriapriori datadata suchsuch asas seasonseason andand typetype of of landland useuse..WhatWhat isis AI?AI?..


13/66

AI [Artificial Intelligence] in the Crop ClassificationAI [Artificial Intelligence] in the Crop Classification

ItIt makesmakes useuse of of If If.......... ThenThen .......... ElseElse........ StatementsStatements toto decidedecide thethecropcrop forfor thethe polygonpolygon dependingdepending onon thethe cropcrop areaarea asas givengiven bybyStatisticalStatistical datadata. .

CropsCrops areare distributeddistributed inin thethe descendingdescending orderorder of of theirtheir cropcrop areaarea atatthethe districtdistrict levellevel.. TheThe polygonspolygons areare consideredconsidered successivelysuccessively inin thethe

AsAs thethe majormajor cropcrop getget distributeddistributed toto LargeLarge polygonspolygons thethe chanceschancesof of selectinselectin lar elar e ol onsol ons furtherfurther reducesreduces. .

LandLand useuse agriculturalagricultural polygonspolygons areare generatedgenerated basedbased onon similarsimilarCropCrop representedrepresented byby aa valuevalue of of NDVINDVI inin thethe areaarea andand soso itit isisimpliedimplied..

TheThe largerlarger polygonspolygons inin anan orderorder areare toto majormajor cropscrops.. SmallSmall polygonspolygonsgetget classifiedclassified intointo majormajor cropscrops dependingdepending onon thethe terminalterminal areaarea

requiredrequired toto meetmeet thethe districtdistrict levellevel statisticalstatistical cropcrop areaarea. .


14/66

Stages of classification of Agricultural LandsStages of classification of Agricultural Lands

Cro Crop

Name Area

kHa

Arecanut 7.9Coconut 87.4Cotton 1.2

Ground 182.8

Maize 8.1Paddy 58.9Ragi 300.4

Tumkur District Crop Statistics

.

Crop Area

(kHa)

Coconut 5.9Ground Nut 16.9Ra i 23.1

Maize 0.99Paddy 1.2Total 48.1

Tiptur Taluk Biomass

Power


15/66

AI [Artificial Intelligence] in the Crop Classification AI [Artificial Intelligence] in the Crop Classification ((contdcontd))

cropcrop if if aa fuzzyfuzzy linguisticlinguistic logicallogical levellevel isis knownknown. . ForFor ee..gg.. CoffeeCoffee &&CardamomCardamom areare growngrown inin highhigh vegetationvegetation areasareas suchsuch asas ForestForest isisoneone sucsuc uzzyuzzy statementstatement. .

ThisThis cancan bebe resolvedresolved withwith quantizedquantized fuzzyfuzzy levelslevels usingusing growngrown areaarea. .g erg er areaarea xesxes aa grea ergrea er pro a ypro a y n exn ex oror compara ve ycompara ve y aa

largerlarger polygonpolygon. .


16/66

Sources and PartnersSources and Partners


17/66

The Main Data SourcesThe Main Data Sources

RRSSCRRSSC ISRO,ISRO,RemoteRemote

Sensing DataSensing Data

NFPNFP

IntegrationIntegration

AIs andAIs and

Resource Atlas Resource Atlas (CGPL, IISc)(CGPL, IISc)

onsu an sonsu an sTaluk/District Taluk/District

Level DataLevel Data

District LevelDistrict LevelAgri. Stat.Agri. Stat.

DataData


18/66

Types of BiomassTypes of Biomass BioBiowasteswastes cancan bebe classifiedclassified intointo fourfour broadbroad classesclasses basedbased onon thethe

wayway itit isis generatedgenerated asas followsfollows::

AgroAgro BiomassBiomass oresores omassomass WastelandWasteland BiomassBiomass

TheThe BiomassBiomass mapsmaps forfor thesethese classesclasses areare donedone onon differentdifferent layerslayers of of

GISGIS.. AgroAgro BiomassBiomass isis thethe bybyproductproduct of of thethe growngrown cropscrops.. InIn MostMost of of

thethe casescases thethe quantityquantity of of BiomassBiomass isis relatedrelated toto quantityquantity of of CropCrop.... ..

cancan bebe relatedrelated toto itit.. SimilarlySimilarly CoconutCoconut frondfrond isis notnot relatedrelated toto CoconutCoconut ProductionProduction. . LetLet usus nownow LookLook intointo howhow AgroAgro BiomassBiomass isis assessedassessed


19/66

How to Compute Biomass from Crop Spatial AreaHow to Compute Biomass from Crop Spatial Area CropCrop YieldYield isis thethe averageaverage CropCrop growngrown inin TT perper HectareHectare basedbased onon

measurementsmeasurements mademade onon samplesample setssets inin aa regionregion.. ResidueResidue GenerationGeneration kTkT==

CRRCRR (Crop(Crop ResidueResidue Ratio)Ratio) == wherewhere ResidueResidue YieldYield isis

thethe averageaverage ResidueResidue generatedgenerated perper HectareHectare andand CropCrop YieldYield isis thetheaverageaverage CropCrop growngrown perper HectareHectare basedbased onon measurementsmeasurements mademade onon

..

==ResidueResidue GenerationGeneration isis estimatedestimated forfor BiomassBiomass suchsuch asas CoconutCoconut fronds,fronds,CottonCotton stalks,stalks, etcetc..,, byby knowingknowing thethe residueresidue yieldyield (T/Ha)(T/Ha) andand spatialspatialareaarea ((kHakHa))..


20/66

How to Compute Biomass from Crop Spatial Area How to Compute Biomass from Crop Spatial Area (contd..)(contd..) UtilizationUtilization isis kTkT of of BiomassBiomass usedused forfor thethe purposespurposes of of Thatching,Thatching,

Fodder,Fodder, andand DomesticDomestic FuelFuel.. BiomassBiomass SurplusSurplus kTkT==

Where Where URUR= =

Power Potential Power Potential MWYrMWYr= =

Where FFP is Where FFP is Factor for Power= Factor for Power=

Where kgWhere kgperper Energy is found empirically which depends on Energy is found empirically which depends on moisture and ash content in the biomass.moisture and ash content in the biomass.

. .


21/66

Process of Making AgroProcess of Making Agro Biomass Digital AtlasBiomass Digital Atlas

Data Preparation

Is DataComplete

& OK?Verification

Integrate Data

Prepare Map

Use a Grid at district level to Analyze and locate

places of high

Spatially Distribute

resources

Map Data ExtractIs

DataOK?

Preprocessing

Launch on Web


22/66

NonNon Spatial Statistical Data at District LevelSpatial Statistical Data at District LevelAgroAgro cropcrop statisticsstatistics isis availableavailable atat districtdistrict levellevel.. BiomassBiomass generationgeneration fromfrom thethe cropscrops

Ratio=ResidueRatio=Residue YieldYield T/HaT/Ha // CropCrop YieldYield T/Ha)T/Ha).. FollowingFollowing isis aa tabletable showingshowing AgroAgro BiomassBiomass statisticsstatistics forfor thethe districtdistrict of of TumkurTumkur::


23/66

Classification of Agricultural Lands based on district level Classification of Agricultural Lands based on district level Crop StatisticsCrop Statistics

Crop Name Area

kHa

Arecanut 7.9Coconut 87.4

Cotton 1.2Ground 182.8Maize 8.1Paddy 58.9Ra i 300.4

Tumkur District Crop Statistics

Total 646.7

Crop Area

(kHa)

Coconut 5.9Ground Nut 16.9

Ragi

23.1Maize 0.99Paddy 1.2Total 48.1

Tiptur Taluk Biomass

Power


24/66

Extraction of Spatially Distributed AgroExtraction of Spatially Distributed Agro Biomass Data of Biomass Data of TumkurTumkur

Crop Residue Area (kHa)Crop

ProductionBiomass

GenerationBiomassSurplus

PowerPotential

ResidueWise Data - State : Karnataka ; District : TUMKUR ; Year : 2000-01 ; Annual

(kT/Yr) kT/Yr (kT/Yr) (MWe)

Coconut Fronds 87.5 411.3 350.0 175.0 24.5Coconut Husk & pith 87.5 411.3 218.0 109.0 14.2Ground nut Stalks 182.9 179.9 326.0 97.8 12.7Coconut Shell 87.5 411.3 90.5 45.2 6.3Ground nut Shell 182.9 179.9 48.9 29.3 3.5

Ragi Straw 301.0 451.5 586.3 29.3 3.5. . . . .Maize Stalks 8.1 19.4 36.3 14.5 1.9Paddy Straw 58.9 219.0 306.3 15.3 1.8

Arecanut Fronds 8.0 10.3 23.9 8.4 1.2aze o s . . . . .

Arecanut Husk 8.0 10.3 8.3 2.9 0.4Cotton Stalks 1.3 2.0 3.5 1.4 0.2Total 647.7 1293.5 2048.7 559.5 73.9


25/66

Extraction of Spatially Distributed AgroExtraction of Spatially Distributed Agro Data in Data in TipturTiptur


Production(kT/Yr)

BiomassGeneration

kT/Yr

BiomassSurplus(kT/Yr)

PowerPotential(MWe)

- -

oconu ron s . . . . .Ground nut Stalks 16.9 16.6 31.4 9.4 1.2

Coconut Husk & pith 5.9 27.9 14.8 7.4 1.0. . . . .Ground nut Shell 16.2 15.8 4.8 2.9 0.3Ragi Straw 23.1 34.7 45.1 2.3 0.3Maize Stalks 1.0 2.4 4.8 1.9 0.3

Maize Cobs 1.0 2.4 1.2 0.8 0.1Paddy Husk 1.2 4.4 0.9 0.5 0.1Total 48.1 86.0 132.8 40.2 5.3


26/66

Future Method for Spatial Land use for Agricultural landsFuture Method for Spatial Land use for Agricultural lands UseUse of of NDVINDVI andand RainfallRainfall forfor thethe LandLand useuse DataData cancan

enhanceenhance thethe reliabilityreliability of of thethe cropcrop distributiondistribution aaresearchresearch conceptconcept testedtested toto bebe satisfactorysatisfactory inin selectiveselectivecasescases anan oo ee a ap ea ap e ur ngur ng ee nexnex eve opmeneve opmenphasephase. .

TheThe wetwet cropscrops usuallyusually showshow higherhigher NDVINDVI && requirerequire anan

higherhigher RainfallRainfall rangerange thanthan drydry cropscrops.. ThisThis isis thethe scientificscientificprincipleprinciple onon whichwhich thethe NeuralNeural NetworkNetwork willwill dodo thetheclassificationclassification of of LandLand useuse intointo CropCrop AreaArea..

AA setset of of knownknown datadata setssets withwith groundground truthtruth setssets thethe

cropscrops wherewhere cropscrops areare unknownunknown..


27/66

Future Method for Spatial Crop DistributionFuture Method for Spatial Crop Distribution ForFor thethe purposepurpose ArtificialArtificial NeuralNeural NetworksNetworks (ANN)(ANN)

OrOr ParametricParametric FuzzyFuzzy ClassifiersClassifiers oror BothBoth areare usedused..

NeuralNeural NetworksNetworks needneed toto bebe trainedtrained withwith knownknowndatadata setssets beforebefore itit isis usedused forfor speciesspecies classificationclassificationforfor thethe LandLand useuse polygonspolygons. . ThisThis isis donedone byby BackBack

propagationpropagation (BP)(BP)..

NNNN isis usuallyusually multimulti variatevariate. . ItIt takestakes inin multiplemultiple

c arac er s csc arac er s cs ww aa numer canumer ca va ueva ue a ac ea ac e ooitit andand generatesgenerates a a numbernumber basedbased onon thethe weightweight ageage g veng ven oo eaceac c arac er s cc arac er s c.. nn umanuman

analogyanalogy followsfollows..


28/66

Simple Analogy for Neural NetworkSimple Analogy for Neural Network

ac ropaga on or ee ac

Feed forward signal

y Classified Output

e

X1 & X2 Bivariate parameters of the object to be detected

Brain learns

Pressure Difference

Neurons Fire Carry Forward the Signals

and classifies Pressure

levels

Back Propagation or Feed back

Feed forward signal

Apply Pressure Classified Output

Input Pressure as Mono variate parameter


29/66

Some of the Project ContentsSome of the Project Contents TheThe polygonspolygons handledhandled 00..0101 00..55 millionmillion perper statestate

AboutAbout 2525 mainmain andand 300300 subsub layerslayers createdcreated inin thethe GISGIS..

AboutAbout 400400 modularmodular toolstools developeddeveloped ininhousehouse (about(about00..11 millionmillion lineslines of of code)code)..

aboutabout 2020,,000000 polygonspolygons isis 1818 2424 hourshours onon aa highhigh endend

AA statestate withwith aboutabout 2020 districtsdistricts takestakes aboutabout 3344 weeksweeksoo pro uct onpro uct on runt merunt me. .


30/66

Project Contents ( Project Contents ( ContdContd)) IncreasingIncreasing computationalcomputational speedspeed byby rere groupinggrouping similarsimilar CropCrop

polygonspolygons;; thisthis enhancesenhances thethe responseresponse timetime byby 1010 toto 100100 timestimes..

ReductionReduction of of polygonspolygons helpshelps inin producingproducing aa useruser versionversion thatthat cancanbebe handledhandled easilyeasily withwith entryentry levellevel computerscomputers. .

ToolsTools areare developeddeveloped toto analyzeanalyze andand derivederive coefficientscoefficients fromfrom thethesurveysurvey data,data, withwith enhancementsenhancements in in reliabilityreliability andand consistencyconsistency toto bebeusedused forfor estimationestimation of of thethe biomassbiomass residueresidue generation,generation, utilizationutilizationandand otentialotential forfor owerower enerationeneration andand toto embedembed themthem suitablsuitabl ininthethe GISGIS layerslayers forfor spatialspatial representationrepresentation andand queryquery responsesresponses. .

AboutAbout 900900 manman monthsmonths overover aa periodperiod of of 66yrsyrs havehave gonegone inin forfor thethe

developmentdevelopment of of softwaresoftware toolstools andand productionproduction of of AtlasAtlas..

FollowingFollowing fewfew SlidesSlides ShowShow MapMap clippingsclippings andand BiomassBiomass PowerPowerPotentialPotential TabulationsTabulations forfor thethe statestate of of KarnatakaKarnataka ..


31/66

Spatial view of Agricultural Residues in KarnatakaSpatial view of Agricultural Residues in Karnataka

CGPL te


32/66

Demography view of KarnatakaDemography view of Karnataka

AgAg Bi S l t tBi S l t t ii


33/66

AgroAgro Biomass Surplus, stateBiomass Surplus, state wisewiseState-wise Biomass Data - Year : Based on Survey Data [2002-04] ; Annual

Crop Biomass Biomass Power

(kT/Yr) kT/Yr (kT/Yr) (MWe) Andhra pradesh 6021.5 28345.7 21569.8 3947.7 481.3 Assam 2586.6 5945.4 6625.1 1361.7 163.1Bihar 5833.1 13817.8 20441.8 4286.2 530.3Chattisgarh 3815.5 6142.8 10123.7 1907.8 220.9

Goa 156.3 554.7 928.5 180.5 22.7Gujarat 6519.0 20635.5 25471.3 8352.7 1131.1Haryana 4890.2 13520.0 26581.1 10105.9 1303.5

. . . . .Jammu & kashmir 368.7 648.7 1198.7 237.7 31.8Jharkhand 1299.8 1509.0 2191.2 567.7 66.8Karnataka 7356.0 38754.1 26949.3 7814.2 1041.3Kerala 2058.4 9773.3 13072.6 7528.7 1017.9Madhya pradesh 9937.0 14166.9 28348.7 9283.6 1240.2Maharashtra 15542.3 51665.4 39348.6 12998.5 1751.1Manipur 72.6 159.4 318.8 31.9 4.1Meghalaya 0.8 14.0 42.0 8.4 1.1

aga an . . . . .Orissa 2436.6 3633.3 5350.4 1163.4 147.3Punjab 6774.3 31698.9 50187.9 24637.5 3145.4Rajasthan 10478.5 12762.9 25234.4 7419.9 975.0Tamil nadu 2561.5 24688.4 17459.2 7400.8 967.2Uttar pradesh 12672.5 46841.9 50622.1 11869.8 1496.6Uttaranchal 66.4 135.8 159.9 51.6 6.6

West bengal 5575.6 21062.8 23332.7 2968.0 369.5Total 107760.7 347893.5 398375.4 125139.4 16245.7

AgroAgro Biomass Surplus Major ResiduesBiomass Surplus Major Residues


34/66

AgroAgro Biomass Surplus, Major Residues Biomass Surplus, Major Residues [Power potential > 500MWyre][Power potential > 500MWyre]

NationWide Residue wise Data;Annual [>=500MWyre]

Crop Residue Area (kHa)

Production

(kT/Yr)

Generation

(kT/Yr)

Surplus

(kT/Yr)

Potential

(MWe)Paddy Straw 40879.7 89566.6 115921.6 26904.9 3227.2

. . . . .Wheat Stalks 21913.2 60946.4 90417.4 15861.4 2062.1Wheat Pod 21913.2 60946.4 18048.3 8084.6 1131.8Paddy Husk 40879.7 89566.6 15466.1 10264.2 1129.1Cotton Bollshell 8038.8 5743.5 6068.1 4347.0 608.6Cotton Husk 8038.8 5743.5 6068.1 4347.0 608.6Maize Stalks 6231.5 11550.8 21113.9 4182.2 543.7Banana Residue 106.6 3978.9 11885.9 4167.9 541.8Coconut Fronds 1813.4 5973.5 7219.9 3603.6 504.5

Total 78983.2 177759.6 322195.9 98181.2 12655.9

AgroAgro Biomass Surplus Minor ResiduesBiomass Surplus Minor Residues


35/66

AgroAgro Biomass Surplus, Minor Residues Biomass Surplus, Minor Residues [Power potential 100 to 500MWyre][Power potential 100 to 500MWyre]

NationWide Residue wise Data;Annual


Production (kT/Yr)

Biomass Generation

(kT/Yr)

Biomass Surplus (kT/Yr)

Power Potential (MWe)

Soyabean Stalks 6046.3 5820.6 9863.1 3257.1 423.4

Mustard Stalks 3935.0 3902.0 6591.2 2986.4 388.2Tapioca Stalks 205.8 5498.9 3398.2 2377.4 309.1Maize Cobs 6231.5 11550.8 4824.9 1835.4 257.0Bajra Stalks 8312.0 5976.8 11649.1 1864.7 242.4Jowar Stalks 9267.4 9986.0 14191.8 1738.2 226.0Ground Nut Stalks 6524.0 6503.8 11391.5 1708.9 222.2

Sugarcane Tops & Leaves 2669.2 174238.1 8301.6 1517.6 212.5. . . . .

Coconut Husk & Pith 1813.4 5973.5 3113.4 1556.7 202.4Black Pepper Stalks 203.8 4673.2 2336.0 1401.6 182.2Rubber Primary Wood 498.5 0 1495.1 1196.1 167.4Coffee Prunin & Wastes 350.0 266.3 1383.7 1106.9 155.0

Coconut Shell 1813.4 5973.5 1274.6 902.5 126.3Ground Nut Shell 6524.0 6503.8 1611.2 1027.8 123.3Gram Stalks 5928.4 4667.6 4641.8 921.0 119.7Bajra Cobs 8312.0 5976.8 1865.3 884.0 114.9

Total 51985.2 239057.5 91910.4 27789.3 3683.0

AgroAgro Biomass Surplus Minor ResiduesBiomass Surplus Minor Residues [Power potential 10 to 100MWyre][Power potential 10 to 100MWyre]


36/66

AgroAgro Biomass Surplus Minor Residues Biomass Surplus Minor Residues [Power potential 10 to 100MWyre][Power potential 10 to 100MWyre]NationWide Residue wise Data;Annual

Crop Biomass Biomass Power

(kT/Yr)

(kT/Yr)

(kT/Yr)

(MWe)Arhar Stalks 2777.5 2070.6 4418.5 768.3 99.9Castor Seed Stalks 526.0 413.4 1622.8 730.2 94.9Jowar Husk 9267.4 9986.0 1620.4 770.5 92.5Rubber Secondary Wood 498.5 0 995.2 597.1 83.6Til Stalks 1225.3 1024.6 1891.2 642.7 83.6Tea Sticks 573.6 1066.5 909.5 582.1 81.5Safflower Stalks 295.4 160.0 470.6 376.5 48.9

. . . . .Arecanut Fronds 262.8 265.4 769.3 269.3 37.7Arhar Husk 2777.5 2070.6 464.5 232.3 27.9Moong Stalks 1300.8 2408.4 2043.8 204.4 26.6Casurina Wood 21.2 0 208.9 177.6 24.9Ragi Straw 1453.9 2070.6 2329.4 197.6 23.7Guar Stalks 266.3 116.0 231.2 161.8 22.7Potato Leaves 119.6 1095.3 792.4 158.1 22.1Urad Stalks 1458.0 1876.6 1471.1 154.4 20.1

. . . . .

Eucalyptus Residue 16.3 3.1 160.7 136.6 19.1Sun Flower Stalks 1331.0 697.5 870.3 125.0 16.2Moong Husk 1300.8 2408.4 261.2 130.6 15.7Urad Husk 1458.0 1876.6 252.8 126.1 15.1Pulses Stalks 1874.8 1069.2 1142.5 114.3 14.9Oilseeds Stalks 341.9 458.8 882.4 95.6 11.5

Horse Gram Stalks 418.0 764.5 789.4 79.0 10.3Total 32819.3 32332.6 27646.9 7350.7 957.2

S i l i f A i l l R id i I di (S i l i f A i l l R id i I di (M AM A & ISRO& CGPL)& ISRO& CGPL)


37/66

Spatial view of Agricultural Residues in India (Spatial view of Agricultural Residues in India (MoAMoA & ISRO & CGPL)& ISRO & CGPL)

Wh t i E ti l B d thi ?Wh t i E ti l B d thi ?


38/66

What is Essential Beyond this ?What is Essential Beyond this ? TheThe agriculturalagricultural cropcrop patternpattern isis notnot stablestable inin generalgeneral andand requiresrequires atat

leastleast 5566 yearsyears of of cyclescycles toto bebe embeddedembedded toto makemake aa reliablereliableinterpretationinterpretation of of thethe biomassbiomass resourceresource forfor aa powerpower projectproject. .

TheThe utilizationutilization alsoalso needsneeds aa selectiveselective samplesample surveysurvey studiesstudies atatplacesplaces wherewhere thethe studystudy isis alreadyalready conductedconducted toto seesee thatthat thethe datadata

InclusionInclusion of of BiomassBiomass outcomeoutcome fromfrom UrbanUrban SolidSolid Waste,Waste, ForestForest andandWasteWaste LandLand anan extensionextension of of forestforest inin termsterms of of biomassbiomass isis aasignificantsignificant enhancementenhancement to to makemake thethe atlasatlas coveringcovering thethe BiomassBiomassResourcesResources of of thethe countrycountry realisticallyrealistically. .

ThisThis needsneeds additionaladditional inputsinputs fromfrom RSDRSD andand relatedrelated processingprocessing totoembedembed themthem toto thethe atlasatlas.. TheThe existingexisting designdesign isis mademade soso toto acceptaccept

..

The Str teg Adopted for Forest & W ste l nd Biom ssThe Str teg Adopted for Forest & W ste l nd Biom ss


39/66

The Strategy Adopted for Forest & Waste land BiomassThe Strategy Adopted for Forest & Waste land Biomass

-- --completedcompleted earlierearlier isis takentaken asas thethe stagestage forfor furtherfurtherprocessingprocessing. . AgroAgro--biomassbiomass- -powerpower isis estimatedestimated to to bebe moremorethanthan 1616,,000000 MWMW of of energyenergy perper yearyear acrossacross thethe CountryCountry. .

TheThe residuesresidues availableavailable fromfrom forestforest && wastelandwasteland areare addedaddedonon thesethese datadata layerslayers. . CRRCRR [Crop[Crop ResidueResidue Ratio]Ratio] isis notnotapplicableapplicable inin thethe casecase of of forestforest andand wastelandwasteland residuesresidues. .

WasteWaste- -LandLand isis presentlypresently notnot wellwell culturedcultured withwith anyanybiomassbiomass growinggrowing plantsplants. . BasedBased onon thethe speciesspecies mixmix

..

InIn thisthis case,case, thethe biomassbiomass estimateestimate is is donedone usingusing thethe yieldyield..

Importance of Existing Utilization of BiomassImportance of Existing Utilization of Biomass


40/66

Importance of Existing Utilization of Biomass Importance of Existing Utilization of Biomass from Forest & Waste landfrom Forest & Waste land

` venven ee ne c encyne c ency oo a m n s ra ona m n s ra on anan ee sosocharactercharacter of of thethe politicalpolitical system,system, oneone couldcould generalizegeneralize

,,

thethe towns,towns, whilewhile thethe branchesbranches andand twigstwigs belongbelong toto thethepoorpoor. .

HumanHuman needsneeds forfor biomassbiomass are,are, however,however, notnot restrictedrestricted..

TheThe maintenancemaintenance of of lifelife supportsupport systemssystems isis aa functionfunction

performedperformed mainlymainly byby thethe crowncrown biomassbiomass of of treestrees. . ItIt isisthisthis componentcomponent of of treestrees thatthat cancan contributecontribute positivelypositively

nutrientnutrient cyclescycles..

Importance of Existing Utilization (Importance of Existing Utilization (ContdContd ))


41/66

Importance of Existing Utilization (Importance of Existing Utilization (ContdContd))

productionproduction of of biomassbiomass forfor consumptionconsumption asas fuel,fuel, fodder,fodder,manure,manure, fruits,fruits, etcetc..

SocialSocial forestryforestry asas distinctdistinct fromfrom commercialcommercial forestryforestry isissupposesuppose oo ee correc vecorrec ve a mea me aa ee max m za onmax m za on oothethe productionproduction of of allall typestypes of of usefuluseful biomassbiomass whichwhich

..

TheThe appropriateappropriate unitunit of of assessmentassessment of of growthgrowth andand yieldsyields

of of differentdifferent treetree speciesspecies forfor socialsocial forestryforestry programmesprogrammescannotcannot bebe restrictedrestricted toto woodywoody biomassbiomass productionproduction forforcommercialcommercial useuse.. ItIt must,must, instead,instead, bebe specificspecific toto thethe endend

useuse of of biomassbiomass. .

Importance of Existing Utilization (contd )Importance of Existing Utilization (contd )


42/66

Importance of Existing Utilization (contd...)Importance of Existing Utilization (contd...) Evidently, the crisis in biomass for mulching or animal

feed cannot be resolved by planting trees that are fastgrowing but are absolutely unproductive as fodder.

The assessment of yields in social forestry must includediverse types of biomass which provide inputs to agroecosystems. When the objective of tree planting is the

production of fodder or green fertilizer, it is relevant tomeasure crown biomass productivity.

Keeping these factors in mind Wasteland has to be

developed with Plantations suitable for energy. For the present, species available in Forest area are

cons ere to e exten e to aste an area or t e

purpose of Biomass assessment for Energy.

A sample of yields in terms of different residuesA sample of yields in terms of different residues


43/66

A sample of yields in terms of different residuesA sample of yields in terms of different residues

Coniferous & DeciduousConiferous & DeciduousResidueResidue %%

SStemtem 6565BarkBark 33TwigsTwigs 33

BranchesBranches 33LeavesLeaves 3.53.5

UncertainUncertain 55.5.5

S eciesS ecies

Percentage in total Percentage in total Biomass Biomass (%)(%)

Total Total BiomassBiomass

Stem wood Stem wood and bark and bark

Branches Branches and twigs and twigs ((Tons/ha

)Tons/ha )

Eucalyptus Eucalyptus 8181 1919 17.417.4 ..

Acacia Nilotica Acacia Nilotica 4747 5353 31.631.6Prosopis Juliflora Prosopis Juliflora 3030 7070 32.232.2


44/66

TwigsTwigs BarkBark

BranchesBranches Leaves Leaves Crown BiomassCrown Biomass

Method Adopted for the Assessment Method Adopted for the Assessment


45/66

Method Adopted for the AssessmentMethod Adopted for the AssessmentInitiallInitiall thethe biomassbiomass inin forestforest waswas assessedassessed withoutwithout

consideringconsidering thethe plantationplantation densitydensity forfor aa quickquickanalysisanalysis toto enableenable thethe processprocess developmentdevelopment. .

TheThe mapmap waswas reclassifiedreclassified intointo subsub--classesclasses forfor lowlowdensitdensit && hi hhi h densitdensit areasareas usinusin availableavailable roundroundreferencereference pointspoints. . ThisThis involvedinvolved additionaladditional imageimage

processingprocessing andand spatialspatial classificationsclassifications. .TheThe assessmentassessment waswas reworkedreworked withwith thisthis newnew

classificationsclassifications. .

ItIt isis foundfound thatthat therethere hashas beenbeen aa considerableconsiderable

surplussurplus withwith thisthis approachapproach. .

How to Compute Biomass from Forest & Waste land How to Compute Biomass from Forest & Waste land


46/66

ppSpatial AreaSpatial Area

ResidueResidue GenerationGeneration kTkT==

BiomassBiomass SurplusSurplus kTkT==

WhereWhere URUR==

Assessment of Forest Biomass with Plantation DensityAssessment of Forest Biomass with Plantation Density


47/66

yyState: Madhya Pradesh District: Balaghat, Residue wise

Species Name

Biomass Area kHaResidue

Generation kT/Yr

Generation kT/Yr [Incl.

Forest density]

Biomass Surplus kT/Yr

Biomass Surplus kT/Yr

[Incl. ForestSubclasses]

Power MWPower MW [Incl. Forest

density]

Others leaves 496.9 382.0 196.9 305.6 157.5 42.8 22.1

Others bark 496.9 327.4 168.8 261.9 135.0 36.7 18.9

Others branches 496.9 327.4 168.8 261.9 135.0 36.7 18.9

Others twigs 496.9 327.4 168.8 261.9 135.0 36.7 18.9

Bamboo Stalk 20.6 92.1 47.5 73.7 38.0 10.3 5.3Teak leaves 71.9 56.9 29.3 45.5 23.5 6.4 3.3

Teak bark 71.9 48.8 25.2 39 20.1 5.5 2.8

ea ranc es . . . . . .

Teak twigs 71.9 48.8 25.2 39 20.1 5.5 2.8

Bamboo Leaves 20.6 3.6 1.9 2.9 1.5 0.4 0.2

Total 589.5 1663.2 857.4 1330.6 685.8 186.3 96.1

Dense Forest All lands with tree cover of canopy density of 40percent and above.

Open Forest All lands with tree cover of canopy density between 10to 40 ercent.

Density Classification [FSI]

Mangrove Salt tolerant forest ecosystem found mainly in tropicaland sub-tropical inter-tidal regions.

Scrub All lands with poor tree growth mainly of small orstunted trees having canopy density less than 10

Non-Forest Any area not included in the above classes

Species for Forest & Wasteland (FSI)Species for Forest & Wasteland (FSI)


48/66

p ( )p ( )

Species wise plantation upto 1997 by the state forest departments

SPECIES Area in '000 ha. Percentage

Eucalyptus spp . 1,360.91 8.87Tectona grandis 1,330.09 8.67

Acacia ni lo tica 801.61 5.23 Acacia aur icul ifor mis 564.67 3.68Bamboo 408.09 2.66Pinus r oxburghii 318.54 2.08Dalbergia siss oo 266.58 1.74

Acacia catechu 259.54 1.69Shorea robus ta 250.28 1.63

Gmelina arborea 148.01 0.97 Anacard ium occ identale 141.54 0.92Casurina equisetifolia 133.99 0.87Pinus kesiya 127.12 0.83Cedrus deodara 124.93 0.81Populus spp. 47.48 0.31

om ax ce a . .

Acacia mearns ii 37.56 0.24Picea smithiana, Abies pindrow 16.74 0.11Hevea brasi liensis 12.3 0.08Santalam album 10.58 0.07

, . .Total 15,336.60 100

Madhya Pradesh Spatial Forest and WastelandMadhya Pradesh Spatial Forest and Wasteland


49/66

Madhya Pradesh Spatial Forest and WastelandMadhya Pradesh Spatial Forest and Wasteland

dhdh f dh d h lf dh d h l


50/66

SidhiSidhi of Madhya Pradesh Spatial Forestof Madhya Pradesh Spatial Forest

SidhiSidhi of Madhya Pradesh Spatial Wastelandof Madhya Pradesh Spatial Wasteland


51/66


52/66

Grid on Grid on GujarathGujarath and Biomass Production Index[BPI] and Biomass Production Index[BPI] b d l l f kb d l l f k


53/66

based spatial coloring for Rajkot District based spatial coloring for Rajkot District

Computed Biomass Computed Biomass PolygonIdentifier Taluk Area[kHa] AvgRes idue Yield(T/Ha) BPI373998 Tankara 30.35 0.9029 0.5609


54/66

Production Index Production Index 374030 Jetpur 1.13 0.9029 0.5609375244 Rajkot 1.30 0.6588 0.4299[BPI][BPI] a o . . .374052 Rajkot 1.81 0.6588 0.4299374044 Jetpur 1.15 0.6588 0.4299

374047 Jetpur 4.61 0.6588 0.4299374003 Paddhari 7.18 0.5125 0.3981374028 Lodhika 1.06 0.5125 0.3981374050 Gondal 2.22 0.5125 0.3981374034 Rajkot 0.59 0.5125 0.3981

374056 Gondal 0.26 0.5125 0.3981375258 Rajkot 0.60 0.6588 0.3769374055 Kotdasangani 0.83 0.6588 0.3769

. . .374005 Paddhari 2.38 0.4595 0.3608375297 Rajkot 0.17 0.4595 0.2734374011 Paddhari 2.16 0.5125 0.2603374733 Morvi 0.11 0.5125 0.2603

374032 Gondal 0.12 0.5125 0.2603374025 Gondal 0.07 0.5125 0.2603374024 Jetpur 0.01 0.5125 0.2603374002 Vankaner 0.01 1.1051 0.0823374678 Morvi 1.45 1.1051 0.0823374720 Morvi 1.45 1.1051 0.0823375259 Ra kot 0.01 1.1051 0.0823374013 Paddhari 0.06 1.1051 0.0823

374014 Paddhari 26.06 1.1051 0.0823374015 Rajkot 65.16 1.1051 0.0823374016 Vankaner 84.11 1.1051 0.0823374041 Jetpur 0.58 1.1051 0.0823

. . .374734 Morvi 0.11 0.1138 0.0533374021 Gondal 0.14 0.1138 0.0533375250 Rajkot 0.24 0.4281 0.0297374048 Gondal 0.68 0.7425 0.0062374046 Lodhika 0.21 0.7425 0.0062

World Scenario for ForestWorld Scenario for Forest


55/66

Forest cover and per Capita Availability in Different Regions/ Countries

Re ion Percentage of Forest

Per Ca ita Country

Cover to Lan Area (1995) Forest(ha)

World 26.60 0.64Asia 16.40 0.10

r ca . .Europe 41.30 1.30China 14.30 0.10Pakistan 2.30 0.01Nepal 33.70 0.20Bangladesh 7.80 0.02Sri Lanka 27.80 0.10Indonesia 60.60 0.60Malaysia 47.10 0.80

Philippines 22.70 0.10Japan 66.80 0.20USA 23.20 0.80India 15.70 0.06

State wise Forest & Scrub land Area (FSI & NRSA)State wise Forest & Scrub land Area (FSI & NRSA)


56/66

Spatial view of Forest in India (FSI)Spatial view of Forest in India (FSI)


57/66

Waste LandWaste Land


58/66

Annual Rate of Forest Plantation (FSI)Annual Rate of Forest Plantation (FSI)


59/66

Atlas enhancement by Adding Solid Waste Assessment Atlas enhancement by Adding Solid Waste Assessment


60/66

Atlas enhancement by Adding Solid Waste Assessment Atlas enhancement by Adding Solid Waste Assessment ((ContdContd))TheThe followingfollowing slidesslides showshow tablestables forfor thethe SWSW produceproduce inin


61/66

TheThe followingfollowing slidesslides showshow tablestables forfor thethe SWSW produceproduce inin

metrometro citiescities andand classclass II citiescities of of KarnatakaKarnataka. . If If wewe getget thetheyearlyyearly increaseincrease proportionalproportional toto thethe populationpopulation wewe cancan

forfor mappingmapping purposespurposes. .

Per Capita SW Generation in Indian CitiesPer Capita SW Generation in Indian Cities


62/66

mappingmapping andand assessmentassessment of of SWSW isis beingbeing studiedstudied. .

ThoughThough thethe presentpresent architecturearchitecture of of thethe mapsmaps provideprovide forforupup scalingscaling toto addadd SWSW assessment,assessment, it it isis necessarynecessary thatthatSpatialSpatial yieldyield of of SWSW bebe moremore comprehensivelycomprehensively assessedassessedandand implementedimplemented. .

WhileWhile thethe BiomassBiomass generationgeneration isis moremore Natural,Natural, thetheProductionProduction of of SolidSolid WasteWaste isis moremore HumanHuman relatedrelated andand

concentratedconcentrated in in UrbanUrban areasareas. .

Status of Status of M i i l S lidM i i l S lid

Solid Waste Power Potential in Towns of Karnataka

City 2001 Per

capita generaed

Muncipal Solid waste



Active Biomass (At

Power Potential


63/66

Municipal Solid Municipal Solid City population generaed(kg/day)Solid waste(kg/day)

Solid waste(KT/day)

Solid waste(KT/Yr)

Biomass (At10% moisture)

PotentialMwyre

Waste Waste Generation in Generation in

Bangalore 5701456 0.5 2850728.0 2.85 1040.5 468.2 66.9Mysore 799228 0.25 199807.0 0.20 72.9 32.8 4.7Hubli Dharwad 786195 0.25 196548.8 0.20 71.7 32.3 4.6Mangalore 539387 0.25 134846.8 0.13 49.2 22.1 3.2Belgaum 506480 0.25 126620.0 0.13 46.2 20.8 3.0

KarnatakaKarnatakaGulbarga 430265 0.21 90355.7 0.09 33.0 14.8 2.1Davanagere 364523 0.21 76549.8 0.08 27.9 12.6 1.8

Bellary 316766 0.21 66520.9 0.07 24.3 10.9 1.6Shimoga 274352 0.21 57613.9 0.06 21.0 9.5 1.4Bijapur 253891 0.21 53317.1 0.05 19.5 8.8 1.3Tumkur 248929 0.21 52275.1 0.05 19.1 8.6 1.2Raichur 207421 0.21 43558.4 0.04 15.9 7.2 1.0Bidar 174257 0.21 36594.0 0.04 13.4 6.0 0.9Gadag 154982 0.21 32546.2 0.03 11.9 5.3 0.8

Hassan 133262 0.21 27985.0 0.03 10.2 4.6 0.7Mandya 131179 0.21 27547.6 0.03 10.1 4.5 0.6Udupi 127124 0.21 26696.0 0.03 9.7 4.4 0.6Chitradurga 125170 0.21 26285.7 0.03 9.6 4.3 0.6Kolar 113907 0.21 23920.5 0.02 8.7 3.9 0.6Gangawati 101392 0.21 21292.3 0.02 7.8 3.5 0.5

maga ur . . . . . .Bagalkot 90988 0.21 19107.5 0.02 7.0 3.1 0.4Ramanagaram 79394 0.21 16672.7 0.02 6.1 2.7 0.4Ranibennur 89618 0.21 18819.8 0.02 6.9 3.1 0.4Karwar 75038 0.21 15758.0 0.02 5.8 2.6 0.4

Next SlideNext Slideamra nagar . . . . . .

Madikeri 32496 0.21 6824.2 0.01 2.5 1.1 0.2Total 12019509 1563.2 703.4 100.5

Map..Map..

Mapped Status of Mapped Status of Municipal Solid Waste Municipal Solid Waste


64/66

Municipal Solid WasteMunicipal Solid Waste

enera on n assenera on n ass Cities of KarnatakaCities of Karnataka

Open in Browser

Atlas enhancement by Adding Solid Waste Assessment Atlas enhancement by Adding Solid Waste Assessment


65/66

Status of Municipal Solid Waste Generation in Metro Cities in India (CPCB, 1999)

Sl.No. Metro city Municipal

Population Municipal

solid waste,

(tones/day)

Per capita generated

(kg/day) 1 AHMEDABAD 28,76,710 1,683 0.585 2 BANGALORE 41,30,288 2,000 0.484

, , . 4 BOMBAY 1,22,88,519 5,355 0.436 5 CALCUTTA 1,06,43,211 3,692 0.347 6 COIMBATORE 8,16,321 350 0.429 7 DELHI 84,19,084 4,000 0.475 8 HYDERABAD 40,98,734 1,566 0.382

9 INDORE 10,91,674 350 0.320 10 JAIPUR 14,58,483 580 0.398 11 KANPUR 18,74,409 1,200 0.640 12 KOCHI 6,70,009 347 0.518 13 LUCKNOW 16,19,115 1,010 0.624 14 LUDHIANA 10,42,740 400 0.384 15 MADRAS 47,52,976 3,124 0.657

, , . 17 NAGPUR 16,24,752 443 0.273

18

PATNA

9,17,243

330

0.360

19 PUNE 22,44,196 700 0.312 20 SURAT 14,98,817 900 0.600 21 VADODARA 10,31,346 400 0.388 22 VARANASI 10,30,863 412 0.400 23 VISAKHAPATNAM 7,52,037 300 0.399

Total/Average 6,68,85,287 30,058 0.449

Atlas enhancement by Adding Solid Waste Assessment Atlas enhancement by Adding Solid Waste Assessment ((contdcontd))


66/66

Characteristics (% by Weight)

Characteristics of Municipal Solid Waste Generated by Metro cities (CPCB, 1999)

Sl . N o Name of metro city Paper Textile Leather Plastic Metal Glass

Ash, fine earth and

others Compo

stable matter

1 Ahmedabad 6.0 1.0 3.0 50.0 40.00

2 Bangalore 8.0 5.0 6.0 3.0 6.0 27.0 45.00 3 Bhopal 10.0 5.0 2.0 2.0 1.0 35.0 45.00 4 Bombay 10.0 3.6 0.2 2.0 0.2 44.0 40.00 5 Calcutta 10.0 3.0 1.0 8.0 3.0 35.0 40.00 6 Coimbatore 5.0 9.0 1.0 50.0 35.00 7 Delhi 6.6 4.0 0.6 1.5 2.5 1.2 51.5 31.78 8 Hyderabad 7.0 1.7 1.3 50.0 40.00 9 Indore 5.0 2.0 1.0 49.0 43.00 10 Jaipur 6.0 2.0 1.0 2.0 47.0 42.00

11 Kanpur 5.0 1.0 5.0 1.5 52.5 40.00 12 Kochi 4.9 1.1 36.0 58.00 13 Lucknow 4.0 2.0 4.0 1.0 49.0 40.00 14 Ludhiana 3.0 5.0 3.0 30.0 40.00 15 Madras 10.0 5.0 5.0 3.0 33.0 44.00 16 Madurai 5.0 1.0 3.0 46.0 45.00 17 Nagpur 4.5 7.0 1.9 1.25 0.35 1.2 53.4 30.40

. . . . . . . . 19 Pune 5.0 5.0 10.0 15.0 55.00

20 Surat 4.0 5.0 3.0 3.0 45.0 40.00 21 Vadodara 4.0 7.0 49.0 40.00 22 Varanasi 3.0 4.0 10.0 35.0 48.00 23 Visakhapatnam 3.0 2.0 5.0 5.0 50.0 35.00

Average 5.7 3.5 0.8 3.9 2.1 2.1 40.3 41.80

mapping of bio-waste resources across india

Documents