Spatial Pollution Rose Dispersion Pattern ( SPR ) Of SO2 .
International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 3 Issue 6, June - 2014Spatial Pollution Rose Dispersion Pattern ( SPR )of SO2 in the Vicinity of Thermal Power StationAt Ennore - Manali Area12Dr. S. Palanivelraja2ProfessorDepartment of Civil EngineeringAnnamalai University– ChidambaramTamilNadu,India.G. Praveen Kumar1Research scholar,Department of Civil Engineering,Annamalai University – ChidambaramTamilNadu,India.Abstract-The purpose of the present study was to describe theIJERTpattern of Spatial Pollution dispersion pattern in theneighbourhood of Thermal Power Stations at Ennore – Manaliarea, near North of Chennai. Ennore-Manali area houses twoThermal Power Stations namely Ennore Thermal Power stationsand North Chennai Thermal Power stations. Ennore Thermalpower stations is designed to produce electricity at 450 MWcapacity ( 2 x 60 MW ; 3 X 110 MW ) using coal as the fuel.North Chennai Thermal power stations is designed to produceelectricity at 630 MW capacity ( 3 x 210 MW ) using coal as thefuel. The emission from all the stacks is considered in theGaussian diffusion equation, for predicting ground leveldownwind concentrations. The meteorological data gathered fora period of one month is chosen for predictions. This workinvolves computations and draw the spatial pollution rosepattern of short-term averages of SO2 Concentrations in theneighbourhood of Ennore – Manali area. The SO2 isoplethsindicated for assessing the most adverse meteorologicalsituations would throw further light on future expansionprospects of Industrial projects at Ennore- Manali area.North Chennai industrial area, has registered asuccessful and planned expansion of industries during the pastfive decades. However, a sporadic development of residentialcolonies has also sprung up in the area. As a result, themagnitude and severity of air pollution problems haveattracted the attention of the public. There is a necessity,therefore, to adopt a systematic procedure for controllingquantum of pollutants emitted from each industry located inthe area, in order to maintain the ambient air quality in NorthChennai area, and in the neighborhoods of the industries atNorth Chennai area within safe limits. The first step to beinitiated, in this relevance, is to conduct another Ambient AirQuality Data in the North Chennai area with the purpose ofassessing the presently prevailing air quality. Such an attemptwould be helpful to gather data on aspects such as (i) thevarious industrial sources which emit air pollutants into theatmosphere, (ii) the quantity and nature of emissionsdischarged by each industry, (iii) the wind turbulencecondition that prevails in the area, and (iv) the ambient airconcentrations of various pollutants occurring at breathing –level in the area.Keywords: Sulphurdispersion patternI.Dioxide,SpatialPollutionRoseII. MODELLING OF AIR POLLUTANTDISPERSIONINTRODUCTIONIndustrial air basin, in the contemporary India,experiences a profound change in the nature and extent of airpollution. Factors such as industrial expansion, acceleratedconsumption of products, fuels and energy, the introduction ofnew chemical and petrochemical processing industries, thevastly increased use of automobiles and the growth ofurbanization, have all greatly increased the varieties andvolumes of pollutants thereby presenting new threats tohuman health, animal health, plant life, property value and theEnvironment. This is the very reason why this study has beenchosen to protect the health and welfare of the public, fromthe harmful effects of air pollutants.IJERTV3IS061738The Atmospheric dispersion models are simulatedmathematically. The physical mechanism and chemistry thatare governing the transport, dispersion and transformation ofpollutants in the atmosphere are taken into account in thesimulation. They are the indispensable tools for prediction ofair quality in the ever expanding industrial environments.Several models are available for predicting air quality due toemissions from multiple point sources. When applied for aspecific industrial situation and prevailing ambientenvironmental conditions, performance evaluation of thesemodels is much essential to assess their compatibility andaccuracy. The reliability of the model also must be assessedby applying historical meteorology, varying quantities ofemissions and measured air quality of the specific industrialwww.ijert.org2028
International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 3 Issue 6, June - 2014V. MATERIALS AND METHODenvironment, with respect to the prevailing wind turbulenceand stability conditions of the atmosphere.The ISC Short-Term Model AlgorithmsThe ISCST3 model is a steady-state Gaussian plumemodel, which can be used to assess pollutant concentrationsfrom a wide variety of sources associated with an industrialsource complex. The following options have been selected forregulatory applications: set the regulatory „„default option‟‟;i.e., use the keyword DFAULT, which automatically selectsstack tip downwash, final plume rise, buoyancy induceddispersion (BID), the vertical potential temperature gradient, atreatment for the calms, the appropriate wind profileexponents, the appropriate value for pollutant half-life, and arevised building wake effects algorithm; set the „„ruraloption‟‟ (use the keyword RURAL) or „„urban option‟‟ (usethe keyword URBAN); and set the „„concentrationoption‟‟.ISCST3 uses Briggs (1969, 1971, 1975) plume riseequations for final rise. The Rural dispersion coefficients fromTurner (1970) are used, with no adjustments for surfaceroughness or averaging time. The Buoyancy induceddispersion (Pasquill, 1976) is included. There are six stabilityclasses used. The Mixing height is accounted for multiplereflections until the vertical plume standard deviation equals1.6 times the mixing height and the uniform vertical mixing isassumed beyond that point. The Perfect reflection is assumedat the ground.IJERTIn so far as a particular pollutant is concerned, therelationship between the rate of emission of the pollutantdischarged through the exit-point of the chimney (stack) andthe resulting concentration of the pollutant in atmospheric airat breathing – level is to be evolved. Any Air Quality Surveywould offer the necessary data for evolving the relationshipbetween the emission rate and the resulting ambient airconcentration of a specific pollutant at any receptor point.This relationship would necessarily take into account thefollowing factors, namely, (i) wind speed and wind directionprevailing in the area, and other meteorological parameterswhich are relevant to the measurement of the atmosphericturbulence and the atmospheric mixing conditions that areprevailing (ii) the height of the exit-point through which anindustrial chimney discharges its emissions, and (iii) thevelocity with which the emissions are discharged into theatmosphere through the chimney. (iv) the temperaturedifferential between the hot gases at the time of mixing withthe ambient air etc. This attempt, to correlate the pertinentparameters in a cause – effect relationship, for describing thephysical mechanism of dilution of pollutants in atmosphericair, is often referred to as „Modeling of Air PollutantDispersion‟.Air pollution control field-operations, as practiced inadvanced and industrialized countries, would consist of twosteps, namely, Surveillance and Enforcement. The mission ofAir Quality Management as a whole is to implement plansthat have been adopted to achieve accepted levels of airquality, through the essential steps of Surveillance andEnforcement.III. NEED FOR THE STUDYThe performance evaluation of USEPA ISCST3model is found satisfactory as reported by GanapathySubramanian. L.R.,( 2006) at Manali region in Chennai.However, this study has considered the emission only fromone power plant at Manali - Ennore area. Therefore, detailedstudy is needed for assessing the comprehensive status of theprevailing air pollution scenario in the entire North ChennaiAir Basin, so that the comparatively larger emissions of NorthChennai Thermal Power Stations could be included in theassessment.The Gaussian EquationThe ISC short-term model for stacks uses the steadystate Gaussian plume equation for a continuous elevatedsource. For each source and each hour, the origin of thesource's coordinate system is placed at the ground surface atthe base of the stack. The x-axis is positive in the downwinddirection, the y-axis is crosswind (normal) to the x-axis andthe z-axis extends vertically. The fixed receptor locations areconverted to each source's coordinate system for each hourlycalculation of concentrations. The hourly concentrationscalculated for each source at each receptor are summed up toobtain the total concentrations produced at each receptor pointby the combined source emissions. For a steady-stateGaussian plume, the hourly concentration at downwinddistance x (meters) and crosswind distance y (meters) is givenbelow.IV. OBJECTIVES OF THE STUDYThe objective of the study is to draw SpatialPollution Rose dispersion pattern of SO2 in theneighbourhood of Thermal Power Plants at Ennore and NorthChennai, to describe the air quality in North Chennai AirBasin.WhereQ pollutant emission rate (mass per unit time),IJERTV3IS061738www.ijert.org2029
International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 3 Issue 6, June - 2014K a scaling coefficient to convert calculatedconcentrations to desired units (default value of 1 x106 for Q in g/s and concentration in µg/m3),V vertical term,D decay term, y, z standard deviation oflateral and vertical concentration distribution (m), Us mean wind speed (m/s) at release heightEquation (1) includes a Vertical Term (V), a Decay Term (D),and dispersion parameters ( y and z).Emission InventoryThe emission source information that needs to be theinput to the model is restricted to the physical stackdimensions (height, location, internal diameter), as well as thevelocity and temperature of the released gas, and the hourlySO2 emission rates. NCTPS and ETPS that are responsible forSO2 generation in the area are considered. Table 1 show thestack co-ordinates, stack height, stack diameter at exit point,emission rate of SO2, exit stack gas velocity and exit gastemperature. A typical SO2 emission file for NCTPS stackshas been developed for a period of 28 days.Table-1. Emission InventoryVI. DESCRIPTION OF STUDY AREAThe study region, North Chennai air basin is anindustrial belt covering an area of about 10 x10 km2 with a flatterrain located close to Chennai metropolis, India. It houses amajor refinery, petrochemical, fertilizer and chemicalindustries apart from Ennore Thermal Power Station (ETPS)and North Chennai Thermal Power Station (NCTPS) whosestack emissions contribute significantly to air pollution ofSO2.StackNameNCTPSUnit 1(boiler)IJERTUnit 2(boilerUnit 3(boiler)ETPSStack 1ETPSStack 2ETPSStack 3StackCoordinatesLatLong13 15.036‟N13 15.077‟N13 15.131‟N13 12.082‟N13 12.102‟N13 12.104‟N80 19.727‟E80 19.714‟E80 19.737‟E80 18.708‟E80 18.661‟E80 onrate ofSO2 924804.6110.746804.6121.78Meteorological DataFigure-1: Description of Study Area.VII. APPLICATION OF ISCST3 MODEL AT NORTHCHENNAI AIR BASINThe data requirements for evaluation analysis consistof three important parts: the emission inventory, themeteorological data and the air quality monitoring data.IJERTV3IS061738The model requires the site – specific meteorologicalinformation as input data .It is restricted to the Julian day ofthe year, the average wind flow vector, wind speed, height ofthe mixing layer, ambient air temperature, and the Pasquillstability category. The meteorological file for ISCST3 Modelhas been prepared for 28-days from the meteorologicalmonitoring conducted during a period of 28 days in the NorthChennai Air Basin from 1/2/2011 to 28/2/2011. Themeteorological monitoring consists of hourly wind speed,wind directions, dry and wet bulb temperatures and cloudcover. The most predominant wind direction during the periodwas the wind blowing from North. Therefore, this study haschosen the sampling stations, which are all located on thedownwind directions. The typical meteorological filedeveloped for the 28 days is considered in this study.www.ijert.org2030
International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 3 Issue 6, June - 2014the east of the user-specified origin and the Y axis is positiveto the north.Table - 2: A typical met file format is shown 112222222211111111111111112MIX(R)VIII.RESULTS AND DISCUSSIONMeteorologicalformonitoring has been conductedduring a period of 28 days from 1/2/2011 to 28/2/2011.Thewind rose diagrams have been prepared. SO2 emission ratehas also been worked out and typical values are used in theISCST3 model. The ground level concentrations (GLC) at thereceptor points has been obtained by running the ISCST-3model, Isopleth plot has been marked on a 25km x 25km n this study, the computed results have beencompared with the observed 24-hour averages. During theperiod of study, the most predominant wind direction hasbeen from North ( N ). Hence, this study has chosen seven airquality-monitoring stations such as Kattupalli, Athipattu,Minjur, Perungavoor, Manali, Ennore and Kathivakkam. TheISCST3 model has been used for predicting the concentrationof the pollutant SO2 at the seven sampling stations. Thesesimulations are carried out for 1 set of 24- hourlymeteorological data, which include various combinations ofstability and wind speed, which may be possible during thewhole day.In this Study, the model performance wasevaluated by comparing the monthly predicted concentrationsof SO2 obtainedin the model with the measuredconcentrations, in the cases of the 3- Continuous Ambient AirQuality Monitoring Stations.IJERTY M DHFigure-2 shows scatter–plots for the measured SO2concentrations and the predicted SO2 concentrations in thedownwind receptor locations in the ISCST3y 0.811xR² 0.86535OBSE SR O2VEDAir Quality DataThe ISCST3 also requires input information onmeasured SO2 air quality data measured at North Chennai AirBasin during the same period. Such information is needed totest the performance of the ISCST3 model. This study haschosen the Ambient Air Quality data from the short-term airquality survey conducted at seven AAQ monitoring stationssuch as Kattupalli, Athipattu, Minjur, Perungavoor, Manali,Ennore and Kathivakkam in North Chennai Air Basin. Anaverage time of 24-Hours was employed for the measurementof SO2. All the stacks and air quality station‟s coordinates (X,Y) were determined by assuming that the X axis is positive to302520Series15Linear (Series)1050050PREDICTED SO2model, applicable to North Chennai Air Basin.Figure-2. Correlation between predicted and measured concentrationof SO2IJERTV3IS061738www.ijert.org2031
International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 3 Issue 6, June - 2014The results of the statistical analysis of this Study revealsthat the value of coefficient of correlation r2 0.865 and indexagreement d 0.77, which implies that the model performedwell.CONCLUSIONSIn order to study the prevailing metereologicalpotential at Ennore-Manali, a short-term micro meterologicalmonitoring was gathered from 01-02-2011 to 28-02-2011.The concentrations of pollutant namely SO 2, for the months ofFebruary, 2011 were plotted using ISCST3 model. From themodel prediction it was found that the concentrations ofcriteria pollutants were below the permissible limitsprescribed by Central Pollution Control Board (CPCB). Theresults were shown that the 8 hour SO₂ concentration wellbelow the Ambient Air Quality Standards. ISCST3 Modelinghas been used to simulate the spatial Pollution Rose pattern ofSO2 in North Chennai Air Basin Considering the emissionsfrom North Chennai Thermal Power station (NCTPS) andEnnore Thermal Power Station (ETPS).REFERENCE1.Figure 3:3D Surface View of Spatial Pollution Rose Pattern of SO2 for themonth of February 20112.3.IJERT4.Aggarwal A.L, Sivacoumar R., Goyal SK 1997 : Air QualityPrediction : influence of model parameters and sensitivity analysis,India Journal of Environmental Protection, 17(9), 650-655.Al.Sudairawi.M., Mackay.K.P1986 : Evaluating the performance ofISC-ST model, Environmental Software 3(4), 180-185.Bowers, J.F, et al 1981, An evaluation study for the ISC dispersionmodel / EPA - 450 / 4-81-002, RTP, NC.Briggs, G.A (1973), “Diffusion estimation for small emissions”,ATDL contribution 79, Air Resources, Atmospheric Turbulent andDiffusion Laboratory, Oak Ridge, TN, United States.Briggs, G.A., 1971: Some recent analyses of plume riseobservations. pp 1029-1032 in Proceedings of the SecondInternational Clean Air Congress. , Academic Press, New York.Carpenter, S.B. et. al., (1971), “Principal Plume Dispersion Modelfor TVA Power Plants”, J. Air Polln. Control Assn., 21, pp. 491495.Chockalingam, M.P., (1988): Environmental Impact Assessment inrespect to air quality in the neighborhood of second Thermal PowerStation at Neyveli, a Technical report furnished to the NeyveliLignite Corporation, NeyevliChockalingam, M.P., “A Report on the Ambient Air QualityPrevailing in the Neighbourhhod of the Tuticorin Power Station”.Also “Air Quality Study for a 630 MW Coal-fired Thermal PowerPlant in North Madras” (1986) : Technical Reports submitted to theDepartment of Environment and Forests, Government of India,New Delhi, for obtaining Environmental Clearance for the proposalof the TamilNadu (State) Electricity Board, Madras (TamilNadu,India).GanapathySubramanian,L.R., et.al. (2006): Modeling of SO2emission from point sources in Manali region of Madras, India,International Journal of Applied Sciences, ANSINET publishing,Vol. 6 (15), pp 3035-3043.Goyal P, Sidhartha et al 2002, Effects of winds on SO and SPMconcentrations in Delhi, Atmospheric Env., 36(17), 2925-2930.Hanna, S.R. et. al (1982), “Handbook on Atmospheric Diffusion”,Published by Technical Information Centre, U.S. Department elinesforMicrometeorological Techniques in Air Pollution Studies” (IS :8829 – 1978).Padmanabhamurty, B (1988), “Manual for Environmental ImpactAssessment Due to Thermal Power Plants on Ambient Air Quality”,under a Project funded by the Department of Environment, Forestsand Wild Life, Government of India, New Delhi.Palanivelraja. S., et al, 2001 : Computer aided assessment of worstmeteorological situation, Proc. of National Seminar onEnvironmental Management and Pollution abatement, AnnamalaiNagar, T.N. India.Palanivelraja. S. et al (2009), Spatial Pollution Rose (SPR) patternof SO2 in the vicinity of Thermal Power Station at Neyveli,Journal of IPHE, India, vol. 2009-10, No.3.5.6.7.8.Figure 4:3D Wire Frame View of Spatial Pollution Rose Pattern of SO2 forthe month of February 20119.10.11.12.13.14.15.Figure 5:2D View of Spatial Pollution Rose (SPR) Pattern of SO2 for theMonth of FebruaryIJERTV3IS061738www.ijert.org2032
International Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 3 Issue 6, June - 201416.17.IJERT18.Pasquill, F., 1976: Atmospheric Dispersion Parameters in GaussianPlume Modeling, Part II, Possible Requirements for change in theTurner Workbook values, EPA-600/4-76-030b, US EnvironmentalProtection Agency,Research Triangle Park, NC.Schulman, L.L., et al., 1985: Evaluation of the proposed down washmodifications to the Industrial source complex model,Environmental Research and Technology, Inc., P-B 810-012,Boston, MA.Subbiah.V. et al (2010), Modeling of Air Pollution Impact in theneighbourhood of a Combined Cycle Gas Turbine (CCGT) ThermalPower Plant, Hindustan Journal, vol.3, February 2010 PP 87-94.IJERTV3IS061738www.ijert.org2033
Manali-Manali area houses two Thermal Power Stations namely Ennore Thermal Power stations and North Chennai Thermal Power stations. Ennore Thermal . The physical mechanism and chemistry that are governing the tr
Dispersion is generally divided into two individual contributions, known as material dispersion and waveguide dispersion [2]. Material dispersion is related to the wavelength dependency of the refractive index of silica (SiO2) glass, which is the host material of optical flbres used for transmission purposes. Waveguide dispersion, on the other 1
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Unit 5 : Environmental Pollution Definition Cause, effects and control measures of :- a. Air pollution b. Water pollution c. Soil pollution d. Marine pollution e. Noise pollution f. Thermal pollution g. Nuclear hazards Solid waste Management : Causes, effects and control measures of urban and industrial wastes.
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of the CALINE and ROADWAY pollution dispersion models to estimate a dispersion parameter (in m 2/s). The emissions and dispersion estimates are combined with background concentration 3 (in g/m ) and a vehicle penetration parameter to estimate the exposure concentration on
Abstract. The asymptotic dispersion force F between two graphene sheets at a separation d is unusual: at T 0K,F Cd p,wherep 4, unlike the 2D insulating (p 5) or metallic (p 7/2) cases. Here it is shown that these anomalous low-temperature properties of p are retained even when spatial dispersion
