Presented by: ABRAHAM VARGHESE

1. Assessment Of Air Quality Near Traffic Junctions In BangaloreCity Using Gaussian Based Line Source Models Presented by: ABRAHAM VARGHESE

2. FACTORS CONTRIBUTING TO URBAN AIR POLLUTION • Urban Population growth – unplanned- slums • Increased industrial activity • Small scale industries • High vehicular growth * Rapid increase in vehicle miles traveled * Slow increase of road coverage * Traffic jams • Fuel quality- adulteration • Burning of fossil fuel • Social and Economic constraints in adopting control measures

3. WHY WE NEED TO BOTHER?

5. Air pollution is growing. Not just in Metros • CPCB says that many cities were worse than Delhi as far as particulate pollution is concerned. • In 31 cities monitored, SPM level has recorded critical levels, that is 1.5 times the standards. Worse RSPM levels are increasing and are much above standards in many cities. • According to WHO there is no safe levels for particulate pollution. From health point of view no standard can be defined.

7. Air pollution related human diseases in Trivandrum city

8. MOTIVATION OF THE PROJECT • Serious adverse health effects from vehicular air pollution have led to the adoption of local air quality management system in many metro cities of the world. • Current emissions and monitoring data can be used to validate an dispersion model which can then be used to forecast future changes based upon a range of ‘what if’ scenarios. • Limited studies have been carried out to validate the Gaussian based line source models for Indian mixed traffic conditions to choose best model for air quality management. • In the present study our attempt has been evaluate the Gaussian based line source models at selected roadways in Bangalore city.

9. INTRODUCTION • In the present work, the GFLSM (Luhar and Patil, 1989) and CALINE4 (Benson 1990) have been evaluated for making short-term forecasts of CO and particulate matter concentrations at selected three air quality control regions in Bangalore city to prepare local air quality management strategies. • The best model may be further used as warning and decision making tool during poor meteorological conditions i.e. air pollution episode conditions

10. SCOPE To study the Gaussian based line source models and to identify the best model that could be useful for management of air quality during episodic conditions in Bangalore city which can be considered as a replica for other metro cities.

11. OBJECTIVES • The objectives of the present study are as follows. • AQCRS selection and pollutants identification in Bangalore city. • To study the vehicular pollution scenario at selected AQCRS. • To study the development of Gaussian based line source model for Indian conditions. • Validation of CALINE 4 and GFLSM models at selected AQCRS in Bangalore city and extended to other urban centers. • To study the comparative performance of CALINE4 and GFLSM models in predicting VEES concentrations at selected AQCRS. • To suggest the control measures to bring down the vehicular pollution levels in Bangalore city.

12. DEFINITION OF AIR POLLUTION Air pollution is defined as the presence of pollutants in the atmosphere from anthropogenic or natural substances in quantities likely to harm human, plants or animal life; to damage man-made materials and structures, to reduce visibility or produce undesirable odors, to bring about changes in weather or climate or to interfere with the enjoyment of life or property.

13. SOURCES AND CLASSIFICATION OF AIR POLLUTANTS

14. SOURCES OF VEHICULAR AIR POLLUTANTS The vehicular air pollution sources are mainly categorized into three according to the emissions from vehicles. • Exhaust or Tailor pipe emissions 2. Evaporation emissions: 3. Crank case and Carburetor emissions: • According to a study, automobile emissions from crankcase the fuel tank and carburetor evaporation produce 20% of HC, exhaust tail pipe emitting 100% of CO, 100% of NOx, 100% of Pb and 60% of HC and crankcase blow by 20% of HC

15. TYPES OF VEHICULAR AIR POLLUTANTS • Particulate matter (PM) • Carbon monoxide(CO) • Sulphur dioxide (S02) • Nitrogen dioxide (NO2) • Lead (Pb) • Ozone

16. HEALTH EFFECT OF GASEOUS AIR POLLUTANTS ON HUMANS

19. AIR QUALITY STATUS IN BANGALORE CITY Studied over • 10 Sensitive area • 4 Industrial area • 7 Residential-rural area • 20 Traffic intersections

28. Variations of monthly average of RSPM (µg/m3) concentrations at selected Iocations in the Bangalore city during the year 2000- 2004. (cond..)

29. Variations of monthly average of RSPM (µg/m3) concentrations at selected Iocations in the Bangalore city during the year 2000- 2004.

30. Variations of monthly average of SPM (µg/m3) concentrations at selected Iocations in the Bangalore city during the year 2000- 2004. (cond..)

31. Variations of monthly average of SPM (µg/m3) concentrations at selected Iocations in the Bangalore city during the year 2000- 2004.

32. Variations of 24 hour average of RSPM (µg/m3) concentrations at selected Iocations in the Bangalore city in the year 2000- 2004. (cond..)

33. Variations of 24 hour average of RSPM (µg/m3) concentrations at selected Iocations in the Bangalore city in the year 2000- 2004.

34. Variations of monthly average of SPM (µg/m3) concentrations at selected Iocations in the Bangalore city during the year 2000- 2004. (cond..)

35. Variations of monthly average of SPM (µg/m3) concentrations at selected Iocations in the Bangalore city during the year 2000- 2004.

36. Variations of 24 hour average of RSPM (µg/m3) concentrations at selected Iocations in the Bangalore city in the year 2003.

37. Variations of monthly average of SPM (µg/m3) concentrations at selected Iocations in the Bangalore city during the year 2003.

38. Variations of annual average ambient air quality status (µg/m3) in the Bangalore city during 2000-2004

39. (Source:Neeri,2005)

40. AIR QUALITY MODELSEffective tool for management • Air quality modelling is a way to mathematically simulate atmospheric conditions and behaviour. • It utilises computer programs, using inputs such as meteorology and source emissions. • It can calculate pollutant concentrations in the air or the amount of pollution deposited on the ground from the air. • Appropriate air model is selected based on the type of analysis that is needed. • It provides an efficient way to examine air quality ranging to large areas.

41. AIR QUALITY MODELS • Gaussian • Numerical • Statistical or empirical • Physical • Gaussian based models are the most widely used techniques for estimating the impact of non-reactive pollutants such as CO and SPM.

42. Gaussian Models The Gaussian equation used for air quality model formation given by (Csanady, 1972): The concentration at receptor 'R' due to this line source Where , Q= source emission rate per unit length z= height of the receptor above the ground. H= height of the line source; ú= mean ambient wind speed at source height H, σ`z and σ`y vertical and horizontal dispersion coefficients, respectively and are functions of distance x1 and y1 stability class. The prime (‘) symbol indicates the parameters in wind coordinate system.

43. Selected Air Quality Models • Following are the appropriate air models selected for the study based on the type of analysis that is needed • General Finite Line source model(GFLSM) • For both CO and SPM Predictions • 2. California Line source Model (CALINE4) • For particulate(SPM) Predictions

44. Some features of regulatory models for atmospheric dispersion (2)

45. Development of General Finite Line Source GFLS Model • The GFLSM has been developed by Luhar and Patil (1989) • It uses the relationship between Wind and line source coordinate system derived by Csanady (1972) • This model over comes the finite line source constraint of the GM modal (Chock 1978). • It can also handle all orientations of wind direction with road.

46. GFLS Model for CO predictions GFLSM Equation for CO prediction is as follows: • The basic approach to develop this model is the coordinates transformation between the wind coordinate system (x1, y1, z1) and the line source coordinate system (x, y, z). • L= length of the roadway, '‘= angle between L and the wind vector. • In the line source coordinate system all the parameters viz. x, y, z and L are known from road receptor geometry.

47. GFLS Model for particulate predictions The diffusion without reflection at earth’s surface is important for particulates correction due to no reflection at the earth’s surface, since the ground acts as a sink for them. GFLSM Equation without the reflection term for particulates is assumed as follows:

48. GFLS Model Inputs • Source Strength (Ql) • Wind Speed • Vertical Dispersion Coefficient • Source Height • Stability Class

49. Development of CALINE4 Model • CALINE4 model was developed by the California Department of Transportation and the US Federal Highways Agency (FHA). • It is based on the Gaussian diffusion equation and employs a mixing zone concept to characterize pollutant dispersion over the roadway. • It is a Gaussian model designed for the assessment of traffic emissions from roads. It can model junctions, street canyons, parking lots, bridges and underpasses. • CALINE predicts 1-hour mean concentrations and is therefore useful for investigating episodes of high NO2 and CO concentrations.

50. CALINE4 MODEL FEATURES Caline4 input parameters screen 1. Job Parameters Screen 2. Link Geometry Screen 3. Link Activity Screen 4. Run Conditions Screen