Chapter 3. Materials and Methods

Similar documents
The Climate of Marshall County

METEOROLOGY AND AIR POLLUTION. JAI PRAKASH Civil Engineering IIT Delhi 1 AUGUST, 2011

The Climate of Seminole County

The Climate of Pontotoc County

The Climate of Grady County

The Climate of Payne County

APPENDIX G-7 METEROLOGICAL DATA

The Climate of Bryan County

LOCAL CLIMATOLOGICAL DATA Monthly Summary July 2013

The Climate of Murray County

The Climate of Kiowa County

The Climate of Texas County

LOCAL CLIMATOLOGICAL DATA Monthly Summary September 2016

The Climate of Haskell County

LOCAL CLIMATOLOGICAL DATA Monthly Summary November 2006

May 2017 Weather Summary P a g e 1 Alexander, P.J. & Power, S.

Lecture 7. Science A-30 February 21, 2008 Air may be forced to move up or down in the atmosphere by mechanical forces (wind blowing over an obstacle,

WIND DATA REPORT. Vinalhaven

Weather Station: WH_500_Series. Battery Voltage

Page 1. Name:

5.0 WHAT IS THE FUTURE ( ) WEATHER EXPECTED TO BE?

WIND DATA REPORT. Vinalhaven

JOURNAL OF INTERNATIONAL ACADEMIC RESEARCH FOR MULTIDISCIPLINARY Impact Factor 1.393, ISSN: , Volume 2, Issue 4, May 2014

Module 01 Lecture - 06 Pollution modeling I

Chapter 2 Available Solar Radiation

PREDICTING DAYLIGHT ILLUMINANCE IN URBAN CITY USING STATISTICAL REGRESSION TECHNIQUES

Yucca Mountain climate: Past, present, and future

Format of CLIGEN weather station statistics input files. for CLIGEN versions as of 6/2001 (D.C. Flanagan).

Preface. Keithley Meade Director of Meteorology (Ag.) Antigua and Barbuda Meteorological Service

Which graph best shows the relationship between intensity of insolation and position on the Earth's surface? A) B) C) D)

Life Science Archives (LSA)

Analysis on Factors of Summer Temperature Distribution in the Basin City

Department of Meteorology University of Nairobi. Laboratory Manual. Micrometeorology and Air pollution SMR 407. Prof. Nzioka John Muthama

MAURITIUS METEOROLOGICAL SERVICES

Global solar radiation characteristics at Dumdum (West Bengal)

4.4 MONTHLY WEATHER SUMMARY

SUBJECT AREA(S): science, math, solar power, visible light, ultraviolet (UV), infrared (IR), energy, Watt, atmospheric conditions

330: Daytime urban heat island intensity in London during the winter season

DEPARTMENT OF EARTH & CLIMATE SCIENCES Name SAN FRANCISCO STATE UNIVERSITY Nov 29, ERTH 360 Test #2 200 pts

INFLUENCE OF THE AVERAGING PERIOD IN AIR TEMPERATURE MEASUREMENT

Development of wind rose diagrams for Kadapa region of Rayalaseema

Average Monthly Solar Radiations At Various Places Of North East India

SOFTWARE USER MANUAL. Weather Capture Advance WS1640 WM9280

1. The diagram below represents Earth and the Moon as viewed from above the North Pole. Points A, B, C, and D are locations on Earth's surface.

Analysis of meteorological measurements made over three rainy seasons in Sinazongwe District, Zambia.

Direct Normal Radiation from Global Radiation for Indian Stations

CAMARGO RANCH, llc. CRAIG BUFORD BufordResources.com

LAB 2: Earth Sun Relations

Meteorology. Circle the letter that corresponds to the correct answer

Field Experiment on the Effects of a Nearby Asphalt Road on Temperature Measurement

C) the seasonal changes in constellations viewed in the night sky D) The duration of insolation will increase and the temperature will increase.

ESCI 344 Tropical Meteorology Lesson 7 Temperature, Clouds, and Rain

4. Atmospheric transport. Daniel J. Jacob, Atmospheric Chemistry, Harvard University, Spring 2017

Heat islands over Mumbai as revealed by autorecorded thermograph data

LE Accumulation, Net Radiation, and Drying with Tipped Sensors

Average Weather In March For Fukuoka, Japan

Chapter 3: Temperature

Earth Moon Motions A B1

Worksheet: The Climate in Numbers and Graphs

Lecture 4 Air Temperature. Measuring Temperature. Measuring Temperature. Surface & Air Temperature. Environmental Contrasts 3/27/2012

Agricultural Science Climatology Semester 2, Anne Green / Richard Thompson

FRAPPÉ/DISCOVER-AQ (July/August 2014) in perspective of multi-year ozone analysis

A Model to Determine Atmospheric Stability and its Correlation with CO Concentration

Analysis of Rainfall and Other Weather Parameters under Climatic Variability of Parbhani ( )

Average Weather For Coeur d'alene, Idaho, USA

Temperature mapping, thermal diffusivity and subsoil heat flux at Kariavattom of Kerala

APPENDIX 3.6-A Support Information for Newcastle, Wyoming Meteorological Monitoring Site

Forecasts include: Temperature. Barometric (air) Pressure. Wind direction/speed Humidity

MiSP Astronomy - Seasons Worksheet #1 L2

STUDIES ON BLACK CARBON (BC) VARIABILITY OVER NORTHERN INDIA

5) The amount of heat needed to raise the temperature of 1 gram of a substance by 1 C is called: Page Ref: 69

1 A 3 C 2 B 4 D. 5. During which month does the minimum duration of insolation occur in New York State? 1 February 3 September 2 July 4 December

Practice Questions: Seasons #1

WxChallenge Model Output Page Tutorial

NOTES AND CORRESPONDENCE. Seasonal Variation of the Diurnal Cycle of Rainfall in Southern Contiguous China

Assessment of Mixing Height at Qena/Upper Egypt Based on Radiosonde Data

Meteorology. Chapter 15 Worksheet 1

MAURITIUS METEOROLOGICAL SERVICES

Central Ohio Air Quality End of Season Report. 111 Liberty Street, Suite 100 Columbus, OH Mid-Ohio Regional Planning Commission

MAURITIUS METEOROLOGICAL SERVICES

Urban heat island in the metropolitan area of São Paulo and the influence of warm and dry air masses during summer

AT351 Lab Seven Skew-T Stability Analysis

2016 Meteorology Summary

NAVIGATION THEORY QUESTIONS Basics of Navigation

Studying Topography, Orographic Rainfall, and Ecosystems (STORE)

The Climatology of Clouds using surface observations. S.G. Warren and C.J. Hahn Encyclopedia of Atmospheric Sciences.

MiSP Astronomy Seasons Worksheet #1 L1

ASTRONOMY Merit Badge Requirements

Application and verification of ECMWF products 2011

Northavimet. User Guide. New LLF

ARUBA CLIMATOLOGICAL SUMMARY 2014 PRECIPITATION

MAURITIUS METEOROLOGICAL SERVICES

Guided Notes Weather. Part 1: Weather Factors Temperature Humidity Air Pressure Winds Station Models

Orientation of Building

TAPM Modelling for Wagerup: Phase 1 CSIRO 2004 Page 41

MAURITIUS METEOROLOGICAL SERVICES

ME 476 Solar Energy UNIT THREE SOLAR RADIATION

Dawood Public School Secondary Section Class VII

BIOMETEOROLOGICAL BASIS FOR TOURISM.

GURME The WMO GAW Urban Research Meteorological and Environmental Project

Transcription:

Chapter 3 Materials and Methods

CHAPTER3 MATERIALS AND METHODS The present study aims to identify the role of climatic factors in the dispersal of air pollutants released into the atmosphere at some important places of Bihar. For this purpose meteorological data of eight important stations with different levels of industrialization and urbanization for five year period ( 986-90) have been made use of. Out of these eight stations four each are from plateau region and plains. The eight meteorological stations under study and their abbreviated names are given below: () Patna (PAT) (2) Gaya (GAY) (3) Bhagalpur (BGP) (4) Muzaffarpur (MFP) (5) Dhanbad (DHB) (6) Jamshedpur (JSP) (7) Ranchi (RAN) (8) Daltonganj (DTG) The analysis has been done for the four representative months corresponding to four seasons, viz., winter (January), pre-monsoon (April), monsoon (August), and post-monsoon (October). The required surface meteorological data were collected from the records of IMD, Patna. The surface meteorological data for Patna and Gaya are available three hourly whereas data for the other six stations are only twice in a day i.e. at 03 and 2 GMT. Radiosonde data for five year period (986-90) ofpatna and Ranchi have been collected from IMD, Pune and analysed for upper air wind roses, inversions, mixing heights and ventilation coefficients. For the other six station Radiosonde data are not available. Meteorological data used in this study are as follows: () Surface parameters (i) Temperature (Maximum & Minimum) (ii) Relative Humidity (iii) Rainfall (iv) Wind direction/speed (v) Visibility (vi) Total cloud amount (vii) Lowest cloud height 56

(2) Upper Air Parameters: (i) Temperature (ii) Wind direction (iii) Wind speed Air quality data (80 2, N0 2 and SPM) are available only for Dhanbad and Patna. Air quality data ofdhanbad for the period (987-96) have been collected from Central Pollution Control Board (CPCB), Delhi. In case of Patna, the data were also collected from CPCB, Delhi, which are available for only a period of 99-96. Collected meteorological and pollution data have been analysed and a comparative study among these eight stations and between plains and plateau regions has also been made for 03 and 2 GMT separately. For Patna and Gaya analysis have been done three hourly also. 3. Maximum and Minimum Temperature The percentage frequency of occurrence of daily maximum and minimum temperature (different intervals) ranges have been computed for every seasonal months for all stations separately. The results obtained are shown in the form of bar diagrams. The ranges of mean temperature have been presented in tabular form for all stations. 3.2 Relative Humidity Available relative humidity observations of each station have been alaysed for every seasonal month for 03 and 2 GMT separately. Daily relative humidity at 03 and 2 GMT at various places in Bihar have been averaged out to get average monthly relative humidity. The average monthly maximum and minimum percentage of relative humidity have been presented in tabular form for all stations. The percentage frequency of occurrence of relative humidity (different intervals) have been computed and the results are shown in the form of bar diagrams. 3.3 Rainfall The percentag~ frequency of occurrence of non-rainy and rainy days have been presented in tabular form for all stations. Mean seasonal rainfall has been computed and shown in contour plots for different months separately. The percentage frequency of occurrence of no-rainfall ap.d rainfall (different intervals) have also been computed for every seasonal month for all stations separately and shown in the form of pie charts. 57

3.4 Wind Roses For a given station and period, a star-shaped diagram indicating the relative frequencies of different wind directions, some times also the frequencies of groups of wind speeds in different directions is called wind rose (WHO, 980). The most common form consists of a circle from whose centre are drawn a number of radii to indicate different points of the compass; the length of each radius being proportional to the number of times during the given period that the wind blew from that direction. Wind rose is also defined as a polar plot of the frequency of wind flow as a function of direction and speed at a particular location. The wind speed group considered in the analyses are as follows: Speed group -3 knots I 4-6 knots II 7-0 knots III -6 knots IV 7 & above knots v Class The percentage frequencies of each class in each direction have been computed and the wind roses have been prepared. Direction is taken along 6 compass points (N, NNE, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW, and NNW). Thus it shows the prevailing wind direction. 3.4. Seasonal Wind Roses Winds of each month for the five year periods at both the stations (Patna & Gaya) separately have been computed and the wind roses have been prepared. 3.4.2 Three Hourly Wind Roses The winds observed at every 3 hours in a day in each month for the five year period have been considered and the wind roses have been prepared. The wind roses thus, prepared are for 00, 03, 06, 09, 2, 5, 8, 2 GMT only for Patna and Gaya. 3.4.3 Surface-Wind Roses at 03 & 2 GMT The winds observed twice a day i.e. at 03 and 2 GMT in each month for the five year period have been considered and the wind roses have been prepared separately as mentioned above for all the stations. 58

3.5 Upper Air Wind Roses The wind observed from 950 to 700mb level at 00 and 2 GMT have been considered and the wind roses have been prepared for each level (i.e. at 950 mb, 900 mb, 850 mb, 800 mb, 750 mb and 700 mb level). While presenting upper wind roses, wind speeds are taken in ms- instead of knots as in surface wind roses. Upper wind roses for Patna and Ranchi have only been prepared separately because data were available only for these two stations. 3.6 Atmospheric Stability The atmospheric stability has been estimated by Pasquill's method (Pasquill, 96). This has been made more objective by Turner (964) and Holzworth (974) by specifying the classes according to net radiation index (NR ) and wind speed and by adding an additional class of stability. This method has been used for the stability classification in the present study. The stability classes are ranging from (extremely unstable) to 7 (extremely stable) based on 9 classes of wind speed. Pasquill stability as function of net radiation index and wind speed is also shown in Table 3. Table 3. Stability Class (Turner, 964) Wind speed Net Radiation Index 4 3 2 0 - -2 0, 2 3 4 6 7 2, 3 2 2 3 4 6 7 4,5 2 3 4 4 5 6 6 2 2 3 4 4 4 5 7 2 2 3 4 4 4 5 8, 9 2 3 3 4 4 4 5 0 3 3 4 4 4 4 5 3 3 4 4 4 4 4 ~2 3 4 4 4 4 4 4 Where, =Extremely unstable (A) 2 =Moderately unstable (B) 3 =Slightly unstable (C) 4 = Neutral Condition (D) 5=Slightly stable (E) 6 =Moderately stable (F) 7= Extremely Stable (G) The net radiation index ranges from 4 (highest directed toward the ground) to - 2 (lowest directed away from the earth). Hence, instability occurs during high net radiation and low wind speeds, stable conditions occur with lowest net radiation index and light winds and n~utral conditions occur during cloudy skies or high wind speeds. 59

Since wind speed is already known, calculation of Net Radiation Index (NR) is important for stability classification. 3.6. Calculation of Net Radiation Index (NR): (i) When total cloud amount= 8 oktas (either for day or night time) NR=O. (ii) For night time (period between hour before sunset and hour after sunrise), NR is the function of cloud amount. (a) If total cloud cover is 5o 3 oktas, NR = -2. (b) If total cloud cover is > 3 oktas, NR = - (iii) For day time (period between hours after sunrise to hour before sunset), calculation ofnr requires solar altitude, cloud amount and cloud height. Time for sunset and sunrise is obtained from the table of sunrise and sunset and ' moonrise and moonset. Solar altitude is calculated from the equation Sina = Sinp.Sin~ + Cosp. Cos~. CosT Where, a p = Solar altitude, =Declination of sun, ~ = Geographic latitude, T = S<;>lar hour angle counted for mid day in terms of local app.arent time. Declination of the sun and geographic latitude had been found out from solar aphimeries book. For hour angle true solar time is calculated by the equation:- T (time)= I hour+ Tc + Et Where, T (time) = True solar time, I hour= Civil time, Tc =Longitudinal correction (four minutes for every one degree) and Et = Variable local apparent time. If (T-2) < 0, then hour angle = (T + 2) x 5 degree If(T-2)~0, then hour angle= (T-2) x 5 degree Cloud amount, cloud height, and solar altitude are used to estimate the NR for 60

any particular observational period. The procedure for evaluating NR is given below: a. If cloud amount s 4 and cloud height have any value, then For solar altitude NR 60 <a 4 35 < as 60 3 5 < a s 35 2 a s 5 b. If cloud amount s 4 and cloud height < 2000 meters, then For solar altitude 60 <a 35 < as 60 5 <as35 a s 5 NR 2 c. If cloud amount> 4 and cloud height~ 2000 meters or< 5000 meters then, For solar altitude NR 60 <a 35 < as60 5 < a s 35 a s 5 3 2 The percentage frequencies of occurrence of the seven stability classes for Patna and Gaya have been computed three hourly and presented in bar-diagrams. In addition to this, percentage frequency of occurrence of stability classes at 03 and 2 GMT of all eight stations have been computed and presented in bar diagrams for all representative months. 3.6.2 Stability-Wind Roses The percentage frequency of occurrence of various wind speeds and directions during the unstable, neutral and stable conditions both by day and night have been computed and stability wind roses for Patna and Gaya have been prepared. In addition to this, the percentage frequency of occurrence of various wind speeds and directions 6

during the unstable, and neutral conditions at 03 and 2 GMT have been computed for the other six stations and stability wind roses have been prepared. Since night time data were not available for the other six stations, stable wind roses could not be plotted. Only unstable and neutral wind roses have been prepared. 3. 7 Inversions: An inversion originating from the ground is known as ground based inversion. In case its base starts from some upper level, it is known as an elevated inversion. In case of elevated inversions, vertical mixing in the layer below the inversion may take place, but the inversion may act as a lid, which inhibits vertical motion. Percentage frequencies of ground based inversions and elevated inversions with various top heights have been computed for Patna and Ranchi at 00 and 2 GMT by using radiosonde data. The total number of occasions of ground based inversions in a month for 00 and 2 GMT have been worked out from the series of five year data. From the total number of occurrences of ground based inversions in a month, monthly frequencies have been computed. For the study of percentage frequencies distribution of inversions, the atmosphere within the planetary boundary layer (.5 km.) is considered. The frequencies will be rounded of to the nearest whole number. 3.8 Mixing Height: Mixing height has been determi~ed by Holzworth method (967). It is determined with the help of morning temperature profile from radiosonde observations and surface temperature data. The point at which the dry adiabate through the surface tt:mperature intersects the morning temperature profile, decides the mixing height. Mixing heights have been computed by incorporating heat island intensities. Urban heat island intensities vary from place to place depending on the city size, topography, state of the ground, wind speed etc. To account for the urban heat island intensity at Patna +4 C has been added to early morning minimum temperature and + 2 C to the daily maximum surface temperature except for monsoon month when a value of +2 C has been assumed (Padmanabhamurty & Bahl, 984). Maximum mixing heights for afternoon periods were estimated by extending a dry adiabat from the daily maximum surface temperature profiles. For minimum mixing height, minimum temperature data compiled with incremental temperature 62

due urban and industrial effects was used and the same process was followed for calculating the maximum mixing height also. To account for the urban heat island intensity at Ranchi + 3 C has been added to early morning minimum temperature and + C to the daily maximum surface temperature except for monsoon season when a value of + 2 C has been assumed, because of persistent overcast conditions which considerably reduce the daytime temperature while increasing night time value (Holzworth, 974). Above assumed values for heat island intensity for Patna and Ranchi are in accordance with the studies made at Delhi (Padmanabhamurty & Bahl, 984). Mixing height is the vertical extent above the ground through which vigorous mixing occurs due to mechanical and buoyancy forces. Hence, the larger the vertical extent, the greater is the volume of the atmosphere available for dispersal of pollutants. Three hourly mean mixing heights at Patna have been calculated by intersecting monthly mean vertical temperature profiles at 00 GMT of every day. The mixing heights have been calculated every year from 986 to 990 and average diurnal variation has also been shown in Tables. Mixing height was calculated for every day of each season for five years and the average of maximum and minimum heights have been calculated for Patna & Ranchi separately and has been shown in graphical forms. 3.9 Ventilation coefficient (V.C.): The ventilation coefficient is a parameter that indicates the efficiency of the.. atmosphere in dispersing air pollutants released from local sources. It is a product of mixing height and mean wind speed through mixed layer. It is expressed m 2 s-. Through the mixed layer, the mean wind speeds from 00 GMT radiosonde observation have been" calculated and multiplied with the mixing heights obtained for three hourly at Patna and presented. The mean wind speed through the mixed layer from OOGMT radiosonde observation has been calculated and multiplied by the corresponding mixing height (obtained from maximum and minimum temperature) to get values of maximum and minimum ventilation coefficients and have been shown in graph. The mixing heights and the ventilation coefficients have only been calculated for Patna and Ranchi only because radiosonde data were available only for these two stations. 63

3.0 Visibility: Class 2 3 4 5 6 7 8 9 Percentage frequency of various visibility classes is shown in Table 3.2 Table 3.2 CLASSIFICATION OF VISIBILITY Code Day Light Visibility Night Visibility Objects Visible at 98 20- <50 kms ---------- 97 0- < 20 kms 2.0 Kms. 96 4- < 0 kms 7.5 Kms. 95 2- <4 kms 4.0 Kms. 94 - < 2 kms 2.33 Kms. 93 0.5- < km.34 Km 92 0.2- < 0.5km 0.74 Km 9 0.05 - < 0.2 km 0.33 Km. 90 Objects not visible at 50 m. 0. Km. Visibility is given in coded form as mentioned above. In the present study, percentage frequency of various visibility types have been computed for the four representative seasonal months. Percentage frequency of visibility types of Patna and Gaya have been computed day and night separately. Percentage frequency of visibility types of all stations have been calculated at 03 and 2 GMT separately for the four months. The classification of visibility type is given in the following Table. Type Code I 90, 9, 92, 93, i.e. <94 II 94,95 Table 3.3 Classification of visibility type Day Light Visibility Night Visibility <.0 Km. <.34 Km. > Km but < 4 Kms. >.34 Km. but < 4 Kms. III 96,97,98 i.e. >95 ->4Kms. -- >4Kms. In the present study visibility types I, II and III have been used. Visibility Wind Roses: Visibility wind roses are also drawn for various wind speed classes with visibility type (coded data) for Patna and Gaya. In addition to this, visibility wind roses for other six stations have been drawn separately at 03 and 2 GMT for all the 64

four representative seasonal months. 3. Air Quality Data. Air quality data (S02, N02 & SPM) were collected for the period (987-96) for Dhanbad and (99-96) for Patna from Central Pollution Control Board, Delhi. These data are helpful to assess the existing carrying capacity of the atmosphere and also to elicit whether any new industry can be set up in those localities. Statistics of S02, N02 & SPM concentrations in the ambient air at Patna and Dhanbad (-g/m 3 ) have been collected and presented in tables for both the stations separately. For the status of ambient air quality, monthly, minimum, maximum and mean concentrations of different pollutants were obtained. The target sampling duration for monitoring is 24 hours whereas target frequency of monitoring is twice a week i.e. 04 days in a year. These pollutant values were compared with ambient air quality to assess low, moderate, high and critical zones for industrial and residential areas separately. A comparative study has been done by drawing graph of monthly mean concentration of S02, N02 & SPM in the ambient air at Patna and Dhanbad; the former representing Bihar plains region and the latter plateau region. In the present work, the methods discussed above have been used for the study of "Air pollution climatology of Bihar" and the results of the meteorological and ambient air quality data analysed are discussed in the following chapter 4. 65