Sankaran. S et al. / International ournal of Engineering Science and Technology (IEST) ETEOROLOGICL POTENTIL FOR IR POLLUTNT DISPERSION IN URBN ND RURL RES LONG THE EST COST OF TILNDU SNKRN. S, ssistant Professor in Civil Engineering, nnamalai University, nnamalainagar, Chidambaram- 608 002, Tamilnadu state, India. Email id: ersansme@yahoo.co.in URUGPPN., Professor and Head, Department of Civil Engineering, nnamalai University, nnamalainagar, Chidambaram -608 002, Tamilnadu state, India. Email id: profam@sify.com KNKSBI. V, Former Professor of Civil Engineering, nnamalai University, nnamalainagar, Chidambaram -608 002, Tamilnadu state India. Email id: vkhdce@yahoo.com RENDRN., ssociate Professor in Civil Engineering, nnamalai University, nnamalainagar, Chidambaram -608 002, Tamilnadu state, India. bstract any air pollution episodes occurred in the past decades were associated with the worst meteorological conditions like strong inversion and low mixing height apart from higher emission concentration. Quantitative study of meteorological potential with air pollution in a region is essential for environmental siting of industries, risk assessment for accidental release, etc. Generally, the coastal areas are potential sites for promotion of industrial activities because of the proximity of water and marine transport. Rapid growth of industries and high traffic volume in coastal urban area, Chennai, along the East Coast of Tamilnadu necessitated for the study of exploring the prevailing meteorological potential for air pollutant dispersion. Karaikal, another coastal rural area along the East Coast with industrial and traffic growth was also considered in this study. Upper radiosonde data and surface micrometeorological data relevant to the study areas were acquired from India eteorological Department, Pune. Using the data, the spatial and temporal variations of wind speed and directional changes, the mixing height variation and atmospheric stability classes with respect to time and space, and the nature of inversion and its occurrence were worked out for the two study areas. From this the worst and favourable conditions for air pollutant dispersion were identified. Key words: eteorology, wind speed and direction, mixing height, inversion, stability class, air dispersion Introduction Pollutant dispersion in the atmosphere from point, line, and area sources is mainly depends upon the prevailing micrometeorological character of a region. The assimilation capacity of the atmosphere determines the dilution and dispersion of the pollutants. eteorological potential for air pollutant dispersion is the study of the prevailing meteorological condition in terms of wind speed, solar isolation, mixing height, and atmospheric stability with respect to space and time, and their influences on dispersion and transportation character of air pollutant whenever released into the atmosphere (nil Kumar. K.G., 1999). ISSN : 0975-5462 Vol. 4 No.06 une 2012 2552
Sankaran. S et al. / International ournal of Engineering Science and Technology (IEST) eteorological Potential Experience and investigation have shown that wind speed and atmospheric stability are the weather elements which may be considered as the primary meteorological factors that determine the dilution of air pollutant in the lower atmosphere (Niemeyer, 1960), (Bijendra Rai etal.,1992). The various parameters involved in this study are the wind speed & direction, mixing height, atmospheric stability, and inversion. The study was done using one year (2001) Upper air radiosonde data of Chennai and Karaikal obtained from India eteorological Department (ID), Pune. The two stations Chennai and Karaikal respectively, represent urban and rural areas along the east coast of Tamilnadu. Wind Speed and Direction The fundamental parameters in the movement of contaminants by the atmosphere are the wind speed and wind direction, which in turn are interrelated with the vertical and horizontal temperature gradients in the atmosphere, both large and small scale. long the coasts of continents, the temperature differential between the land and water is sufficient to establish local circulations from the sea to the land during the day and from the land towards the sea during the night (Wexler, 1954). In the East Coast of India, this may be the dominant weather pattern and occur everyday regularly. In addition to the variation of wind flow in the horizontal direction, there is also a marked difference in the vertical direction due to roughness of the earth s surface, called mechanical turbulence, which decreases with altitude. The heating of the earth by the sun induces thermal turbulence, which is at a maximum near the surface and decreases with altitude. Consequently, the rate of dispersion of contaminants is decreased with increasing altitude (agill et al, 1956). Keeping the above in mind, diurnal wind characteristics study had been carried out for each month for Chennai and Karaikal, by taking 3 hourly wind speed and 16 point directions at 02.30, 05.30, 08.30,11.30,14.30,17.30, 20.30, 23.30 LST(Local Standard Time). In wind speed analysis, speed less than1km/hr is considered as calm wind and frequency of occurrence of wind speed at different wind intervals 1-5, 6-11, 12-19, and >19km/hr were worked out and presented in Tables 1 and 2. Pictorial representation of wind characteristics for Chennai and Karaikal stations was prepared in the form of 3-hourly wind roses for the year 2001 in Fig-1 Table 1 Percentage occurrence of Wind Speed during day and night at Chennai Wind speed in km/hr onth Day Night Calm 1-5 6-11 12-19 >19 Calm 1-5 6-11 12-19 >19 an 8.07 12.91 49.20 29.82-55.03 23.40 20.76 0.81 - Feb 7.76 8.62 29.31 50.86 3.45 41.55 17.29 27.59 8.57 - ar 4.84 9.68 34.66 46.78 4.04 33.87 20.14 31.46 13.72 0.81 pr 0.81 5.83 35.00 50.86 7.5 36.41 31.44 12.98 18.84 0.83 ay - 4.06 62.85 30.66 2.43 1.62 10.49 35.46 46.78 5.65 un 3.33 4.20 30.00 54.15 8.32 3.33 8.33 34.16 45.85 8.33 ul 5.65 3.23 37.07 54.05-9.82 9.68 40.96 37.92 1.62 ug 7.50 5.83 28.33 27.54 0.79 16.67 16.67 44.99 21.68 - Sep 3.32 14.16 45.02 37.50-24.97 25.83 40.84 8.33 0.83 Oct 16.13 14.53 51.58 17.76-50.81 17.75 27.40 4.04 - Nov 8.33 24.17 40.83 26.67-45.79 25.00 19.21 9.17 0.83 Dec 6.45 8.87 41.93 41.94 0.81 48.39 13.71 28.22 8.87 0.81 ISSN : 0975-5462 Vol. 4 No.06 une 2012 2553
Sankaran. S et al. / International ournal of Engineering Science and Technology (IEST) Table 2 Percentage occurrence of Wind Speed during day and night at Karaikal Wind speed in km/hr onth Day Night Calm 1-5 6-11 12-19 >19 Calm 1-5 6-11 12-19 >19 an 3.23 7.26 8.06 38.65 42.2 0.81 0.81 8.06 41.87 49.26 Feb 5.18 5.18 11.21 45.79 32.64 3.44 2.58 11.21 50.92 31.84 ar 11.29 7.26 19.30 53.28 8.87 13.74 0.81 20.96 50.81 13.71 pr 5.83 0.83 22.58 57.43 13.33 17.50 2.50 26.67 44.16 9.17 ay - 1.61 4.03 51.61 42.75 - - 12.1 41.93 45.97 un - - 13.33 52.51 34.16 3.33 1.67 21.78 48.22 25.00 ul - 0.81 7.26 55.24 36.29 4.84 1.61 20.16 54.03 19.36 ug 3.23-20.97 48.38 27.42 2.42 1.67 19.36 64.51 12.1 Sep 3.33-35.82 52.22 8.33 10.00 3.33 38.33 43.36 5.00 Oct 16.94 2.42 44.35 33.87 2.42 25.81 1.61 56.45 13.71 2.42 Nov 3.33 3.33 46.68 30.83 15.83 13.33 7.50 42.51 34.99 15.00 Dec 8.06 4.03 29.84 26.60 31.45 3.23 5.65 17.75 33.06 40.32 ixing Height Fig 1 Wind Rose for Chennai and Karaikal The mixing height may be defined as that height above the earth s surface to which released pollutants will extend, primarily through the action of atmospheric turbulence. It is used to determine the amount of vertical mixing possible in the atmosphere. The mixing height may be as high as 1500 to 1850m in the afternoon and only around 100m at night. ixing height for Chennai and Karaikal stations at 00GT (05.30LST) and 12GT (17.30LST) were estimated using radio sonde data by Holzworth(1964) method. Daily morning mixing heights for both the stations were calculated by taking heat island effect of +4 C (Suresh, 2002). Evening heights were estimated using maximum temperature recorded in the relevant station on that day. Then monthly mean mixing height for Chennai and Karaikal at 00GT and 12GT were worked out and presented in Table 3. The graphical representations of monthly mean mixing height for the two stations are shown in Figures 2 & 3. ISSN : 0975-5462 Vol. 4 No.06 une 2012 2554
Sankaran. S et al. / International ournal of Engineering Science and Technology (IEST) Table 3 onthly ean ixing Height at Chennai & Karaikal onth Chennai Karaikal 00GT 12GT 00GT 12GT 492 1339 1779 964 F 760 1356 1048 1033 568 951 724 632 836 1218 829 696 630 2044 662 1112 716 1650 656 852 765 1333 789 1070 1006 1692 580 1086 S 734 1478 720 1322 O 768 1235 762 976 N 740 1522 697 879 D 647 1177 755 792 onthly ean ixing Height for Chennai 2500 00GT 12GT 2000 ixing Height(m) 1500 1000 F S O N D 500 F S O N D 0 0 2 4 6 8 10 12 14 onth Fig 2 onthly ean ixing Height for Chennai ISSN : 0975-5462 Vol. 4 No.06 une 2012 2555
Sankaran. S et al. / International ournal of Engineering Science and Technology (IEST) onthly ean ixing Height for Karaikal 2000 1800 00GT 12GT 1600 1400 S ixing Height(m) 1200 1000 800 600 F S O O N N D 400 200 0 0 2 4 6 8 10 12 14 onth Fig 2 onthly ean ixing Height for Karaikal Inversion Stable atmospheric conditions usually occur when warm air is above cool air and the mixing height is significantly restricted, creating a temperature inversion. ir pollutants released into the atmosphere s lower layer during temperature inversion is trapped there and can be removed only by strong horizontal winds. But high pressure systems often combine temperature inversion conditions and low wind speeds resulting in episodes of severe smog. Conversely, contaminants released aloft from tall chimneys are not generally transported to the ground under these conditions. With the coming of daylight, the ground begins to heat, the inversion gets gradually destroyed, resulting in rapid mixing of contaminant downwards which were released aloft during the night. This often leads to high concentrations during the early forenoon. Chennai and Karaikal are located in coastal plain terrain. Based on radio sonde plot, number of occurrences of surface and elevated inversion for both stations is listed in Table 4. ISSN : 0975-5462 Vol. 4 No.06 une 2012 2556
Sankaran. S et al. / International ournal of Engineering Science and Technology (IEST) Table 4 Number of Occurrence of Inversion Place Chennai Karaikal GT 00 12 00 12 Type of Inversion Ground based Intensity of Inversion F S O N D 0-0.5 C 7 6 2 - - 3 1-3 1 5 6 0.5-1 C 9 7 9 1 4 2 3-2 - - 8 >1 C - 1 - - - - - - 3-2 - Elevated 4 2 4 2 1 - - - - 1 - - Ground based 0-0.5 C - - - - - 2 - - - 2 1 1 0.5-1 C - - - - - - - - 3-1 1 >1 C - - - - - - - - - - - - Elevated 5 2 4 - - 1-1 - - 1 4 Ground based 0-0.5 C 1 1 - - 1 1 1 3 3 3 5 2 0.5-1 C 2 1 2 2 2 2 - - - - 6 6 >1 C - - - - - - - - - - 1 1 Elevated 8 9 11 2 4 - - - - - - 6 Ground based 0-0.5 C 1-1 1 2 2 2 2 1 1 2-0.5-1 C - - - - 1 1-1 - - - - >1 C - - - - - - - - - - - - Elevated 5 2 4 1 - - - - - 2 3 4 tmospheric Stability tmospheric Stability conditions in the atmospheric surface layer can control the distance and direction of transport of air contaminants. It may either encourage or suppress vertical air motion. Stability frequently varies through a wide range in different layers of the atmosphere for various reasons. The oldest and the most commonly used method of categorizing the amount of atmospheric turbulence present was the Pasquill method developed by Turner in 1964. He categorized the atmospheric turbulence into six stability classes, named, B, C, D, E, F and G with class being the most unstable or most turbulent class, and class G, the most stable or least turbulent class. The synoptic hour s surface observation recorded on Chennai for the period of one year was obtained and atmospheric stability classes were determined using solar insolation, cloud cover and wind speed data based on Pasquill stability classes using Turner (1964). The percentage frequencies of seven stability classes during Daytime and Nighttime in all months were computed and presented in Tables 5 and 6 respectively. ISSN : 0975-5462 Vol. 4 No.06 une 2012 2557
Sankaran. S et al. / International ournal of Engineering Science and Technology (IEST) Table 5 Percentage Frequency of Stability Classes during Day Time Stability classes onth B C D E F G an 36.18 42.17 1.61 0.23 0.92 0.69 18.20 Feb 41.63 30.30 2.46 2.46 0.49 0.98 21.68 ar 50.46 25.12 1.15 1.32 1.15 0.46 20.28 pr 30.95 46.42 4.47 3.95 2.38 0.47 14.36 ay 33.18 50.92 4.61 0.46 0.23 0.92 9.68 un 31.19 36.67 9.76 6.20 3.33 2.86 9.99 ul 31.20 35.96 11.82 1.23 1.48 3.20 9.11 ug 21.20 36.18 10.14 10.37 4.38 3.92 13.81 Sep 29.52 47.62 5.71 1.67 2.86 3.10 9.52 Oct 38.25 37.78 3.00 0.46 2.99 3.22 14.30 Nov 33.80 36.43 11.9 1.67 2.86 1.20 12.14 Dec 19.36 49.77 6.91 3.00 4.84 0.92 15.20 Table 6 Percentage Frequency of Stability Classes during Night Time Stability classes onth B C D E F G an - - - 0.81 10.48 16.13 72.58 Feb - - - 2.59 23.28 12.93 61.20 ar - - - 8.06 25.80 16.94 49.20 pr - - - 10.83 31.67 15.83 41.67 ay - - - 16.94 52.42 14.52 16.12 un - - - 24.17 41.67 12.50 21.66 ul - - - 16.13 36.30 21.70 25.80 ug - - - 8.06 17.74 25.00 49.20 Sep - - - 7.50 15.83 15.00 61.60 Oct - - - - 16.13 12.10 71.77 Nov - - - 6.67 11.67 14.17 67.49 Dec - - - 6.45 12.1 15.32 66.13 ISSN : 0975-5462 Vol. 4 No.06 une 2012 2558
Sankaran. S et al. / International ournal of Engineering Science and Technology (IEST) Summary and Conclusion References During anuary, and February, due to the winter season, wind was dominant in the directions of E to NE in both stations and the corresponding dispersion direction was in the W to SW sector of landmass. Sea breeze was a dominant factor in the months of arch, pril and ay (summer season) in directions of ESE, SE and SSE giving way to dispersion in North-West quadrant of land area. Both the stations experience South West onsoon during the month of une, uly and ugust, and North East onsoon during October, November, and December, every year. ccordingly, the dispersion directions are towards seaside and landside respectively. Nocturnal calm wind percentages were high in Chennai when compared to Karaikal. This will allow the build up of the higher pollutant concentration nearby the source location. This phenomenon was highly possible for Chennai during the months of February, arch, pril, October, November, and December. Karaikal was experiencing higher percentages of 12-19km/hr, and >19km/hr wind speed categories during daytime as well as nighttime which would allow more dispersion of pollutants than Chennai. In both Chennai and Karaikal, the morning mixing height was in the range of 492m to 836m in all months except in anuary, February, and pril indicating the possibility of less pollutant dispersion along the downwind direction. The monthly mean mixing depth during evening was above 1000m in all months except arch in Chennai indicating the possibility of high dispersion in that period of time. In Karaikal, the monthly mean mixing depth exceeds 1000m only in few months. The ground-based inversion is the worst meteorological condition for pollutant dispersion when compared with elevated inversion. Of all the months, December, anuary, and February may be the worst period for mixing of pollutants whereas in uly, ugust, and October, the mixing may be better in the study areas. Stability classes in Chennai were evenly distributed in day and nighttime. Due to more percentage of calm wind during nighttime, the occurrence of stable class was more in that period. Highly unstable condition (categories, B and C) had occurred at 06 00GT for all months except in ay, une and uly. The neutral stability condition D occurred during both day and night, but its frequency was very low. [1] nil Kumar, K.G., (1999), ir Pollution Climatology of Cochin for pollution management and abatement planning, ausam Vol. 50, pp.383-390. [2] Bijendra Rai etal., (1992), Dispersion climatology for Patna and Gaya, ausam, Vol.44-2, pp.199-204. [3] Holzworth, G.C., (1967) ixing Depths, Wind Speed and ir Pollution Potential for Selected Locations in United States, Published in ournal of pplied eteorology, Vol.6, No.6, pp. 1039-1044. [4] Niemeyer.L.E., (1960) Forecasting air pollution potential, onwea.rev.,88, pp. 85-96 [5] agill, P.L., etal., (1956), ir Pollution Handbook, c.graw-hill Book Co.,Inc., Newyork [6] Suresh (2000) simple thermo dynamical model to estimate the rate of depletion of nocturnal low-level inversion layer ausam, 51, (2000), 39-46. [7] Turner, D.B., (1964), diffusion model for an urban area,. ppl. et., 3, pp. 83-91 [8] Wexler.H., (1954) Observing the weather from satellite vehicle,. British interplanetary soc., 13, pp. 269-276 ISSN : 0975-5462 Vol. 4 No.06 une 2012 2559