Air Quality Management of Particulate Matter Emitted from Vehicular Sources in Delhi

1. Air Quality Management of Particulate Matter Emitted from Vehicular Sources in Delhi by P. Goyal, Neeru Jaiswal, Rashmi Mittal Centre for Atmospheric Sciences, Indian Institute of Technology, Delhi

2. Overview of the Presentation • Introduction • Objective of the study • Model Formulation • Study Area • Emission Inventory • Results and Discussions • Conclusions

3. Introduction • Delhi is one of the 10 polluted cities of the world. • Particulate Matter (PM) are mainly emitted from industrial process, auto exhaust and domestic sources. • Total number of vehicles registered in Delhi was 4.17 million in 2004. • PM levels are increasing continuously despite number of mitigation measures.

4. Objectives of the Study Air Quality Management of PM in Delhi through: • Development of line source model designed for low and moderate wind conditions. • Detailed source inventory of vehicles. • Forecast of emission of PM as per the existing scenario in next 10 years. • Temporal variation of PM in Delhi. • Spatial variation of PM concentration in Delhi. • Identification of hot spots in Delhi due to PM.

5. Model Formulation The concentration of PM due to line source in low winds (u< 1 m/sec) conditions is C = and the concentration for moderate winds (u> 1 m/sec) is C = Where, C = concentration (mg/m3) ; Q = Source strength (mg/sec) ; U = Wind speed (m/sec) ; (x’, y’, z’) = location of source (m) ; (x, y, z) = location of receptor (m) ; (sx, sy,sz) = dispersion parameters (m) in x, y, z directions respectively.

6. Study Area • A gridded source inventory over an area of Delhi City (26Km × 30Km) has been made. • In each grid, curved roads are approximated by number of straight lines and each of these straight line is considered as a separate source. • The traffic volume for each road has been obtained individually. • Total emission rate of each grid is estimated from the contributions of all the source located within that grid.

7. Emission Inventory Emission rate of PM has been estimated in each grid on hourly basis by using secondary data of vehicular traffic and apportioned according to the following methods: Method I: Where, E = Emission rate (gm/sec) i = Index, varies over six categories of vehicles. Ni = Number of registered vehicles in i th category TDi =Travel demand for vehicular category i (km/day) VKT= Vehicle Kilometer traveled (Km) ei = Emission factor (g/km) for the pollutant of the vehicular category i Vehicles are categorized into 6 vehicle category: Cars, Two wheeler, Three wheeler, Buses, Taxi, and Goods vehicles.

8. Emission Inventory…. • Method II: • Different vehicles were monitored at the major traffic intersections and national highways, nearly at 36 places in Delhi. • Emission rates of PM are estimated by using the emission factors of each pollutant for each vehicle group. • Where, • E = Emission rate(gm/sec) • i = Index, varies over six categories of vehicles • Ni = Number of vehicle of i th category monitored per hour (vehicle/hour) • L = Road length in each monitored grid (km) • ei = Emission factor (g/km) for the pollutant of the vehicular category i • Emission of PM in each monitored grid over the road length in that particular grid is calculated. • The linear interpolation technique is used to estimate the emission made in the rest of the grids of the study area

9. Emission Inventory…. • Method III • Lengths of major, minor and residential roads were estimated in each grid through the measurements. • Monitored traffic data reveals that the traffic volume due to major, minor and residential roads is in the ratio of 43: 33:24 respectively. • Total estimated load of PM on the study area is taken as 12.77 tons/day (CPCB) • Emission of PM in the i th grid is calculated by the following expression: • Where, • Ei = total emission in i th grid • LMi = total length of the major roads in the i th grid • Lmi = total length of the minor roads in the i th grid • Lri = total length of the residential roads in the i th grid • C = total emission of the pollutant in whole study area.

10. Results and Discussion Hourly variation of emission rates of PM has been determined by different methods, which reveals that emission of PM from different methods are well in agreement with observed values. An emission inventory of vehicles has been prepared over the map of Delhi, which gives emission rate of PM in each grid. PM Emission at ITO

11. Results and Discussion Forecasting of PM emissions, on the basis of present emission scenario, for future years (2005-2014) is showing slightly increasing values. EURO-I EURO-II EURO-III EURO-IV PRE-EURO

12. Results and Discussion • Hourly variation of concentration and emission rates of PM shows two peaks during the day hours. First peak occurs between 9 AM to 11 AM and second is between 5 to 7 PM. • A comparative study of model and observed concentration of PM during the day hours shows that model is always over predicting but remains within a factor of two. Temporal variation of PM concentration and Source Strength 4-hourly averaged concentration at Nizzamuddin monitoring station

13. Results and Discussion Isopleths of PM reflects that hotspots in Delhi at ITO, Shivaji Stadium, Cannaught Place, Dhaula Kuan and Laxmi Nagar have maximum values of PM ranging from 300-600 mg/m3 which are beyond NAAQS (200 mg/m3). Spatial variation of PM concentration in Delhi (January, 2004).

14. Conclusions • The present line source model, appropriate for low wind and traffic condition of Delhi can be used for air quality impact assessment due to vehicular traffic in other cities like Delhi. • The emission inventory of Delhi with respect to PM can be used for emission control as a first step for air quality management. • Forecasting of emission of future years may be used for making the future strategy of air quality management. • Model computation of PM shows that concentration of PM are beyond the NAAQS at most of the places in Delhi. • The present isopleths of various concentration of PM can be used for emission control and regulatory purposes.

15. Thanks