International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 7, July 2017, pp. 556–562, Article ID: IJCIET_08_07_059 Available online at http:// http://iaeme.com/Home/issue/IJCIET?Volume=8&Issue=7 ISSN Print: 0976-6308 and ISSN Online: 0976-6316

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ESTIMATION OF PM10 CONCENTRATIONS IN STREET CANYON OF ROAD USING STREET BOX MODEL

Neelam S. Chavan, Rahul Kumar, Milind R. Gidde University College of Engineering, Pune,

ABSTRACT The research involved computing particulate matter concentrations in the street canyon using Mensink and Lewyckyj’s STREET BOX model and assessing the quality of air with regards to the particulate matter concentration for a period of one week for a selected area in the of Pune, India. The pollutant concentration was calculated using the data obtained on hourly basis during the observation week. The effect of variations in wind velocity, wind direction and traffic behavior (for diverse category of vehicles) was studied for this (PM10) pollutant concentration for a defined length of time. Other physical conditions in the street such as average height of building (under the study area), length and width of street gave an idea of the canyon geometry and were used accordingly in the model. Pune-Satara road is one of the busiest and crowded roads in the city of Pune, India. It extends up to a distance of 6.5 kilometers. Pune features among 's worst polluted in a recent World Health Organization (WHO) report. This is attributed to the vehicular emissions that are the major contributor of air pollution. Key words: Street Canyon, Particulate Matter Concentration, Street Box, Pollution, Pollutants, Emissions Cite this Article: Neelam S. Chavan, Rahul Kumar and Milind R. Gidde, Estimation of Pm10 Concentrations In Street Canyon of Pune Satara Road Using Street Box Model, International Journal of Civil Engineering and Technology, 8(7), 2017, pp. 556–562. http://iaeme.com/Home/issue/IJCIET?Volume=8&Issue=7

1. INTRODUCTION Major urban areas in the world are facing a hard time in keeping air pollution under control and a densely populated city like Pune is no exception. There has been a significant increase in the vehicular and industrial emissions due to increase in vehicular population and industries respectively in and around Pune. Traffic generated pollutants include NOx, SOx, CO, Particulate matter, etc. A notable increase in these pollutants in the city has become a matter of immense concern. The percentage of pollutants in the air from vehicular emission has increased manifold as compared to that from other sources .The particulate matter is also high in the city and above

http://iaeme.com/Home/journal/IJCIET 556 [email protected] Neelam S. Chavan, Rahul Kumar and Milind R. Gidde the prescribed level. Particulate matter being small solid particles or liquid droplets suspended in the air not only impact the environment but is also a major cause of health issues such as lung irritation, chronic lung diseases, heart attacks and increase susceptibility to viral and bacterial pathogens leading to pneumonia. The smaller the particles, the deeper they can penetrate into the respiratory system and the more hazardous they are to breathe. Street canyons are considered as hot spots for air pollution problems .The concentration of such pollutants in street canyons need to be kept under checked. In order to carry out efficient traffic and air quality management, validated models and PM emission estimates are needed. (M. Ketzel et al., December 2007).Numerous air models have been developed and used for studying effect on street canyons, that have proved to be useful in urban planning, assessing air quality, pollution exposure, etc. Many such models are STREET BOX Model (Johnson et al., 1973), STREET Model, Canyon Plume-Box Model (CPBM) (Yamartino and Wiegand, 1986) and Operational Street Pollution Model (OSPM) (Berkowicz, 2000; Berkowicz et al., 2008).The purpose of this research is to evaluate the concentration of PM10 in a street canyon and study the effect of various meteorological and physical parameters of the canyon on such pollutant concentrations.

2. MATERIALS AND METHODOLOGY

2.1. STREET BOX Model The concentration inside the urban street canyon can be considered as the result of two contributions, one is emissions from local traffic in the street itself and one from background pollution entering the street canyon from above roof level (L.E. Venegas et al., 2013).The STREET BOX model developed by Mensink and Lewyckyj includes wind direction dependency, but does not necessarily assume re-circulation of the flow in the street canyon Street Box Model developed by Mensink and Lewyckyj:

C= Q + Cb

U∥(H/L)W+(D+IU⊥)(W/H)

Where

C= Calculated concentration of PM10 in the street (μg/m3) 3 Cb=Background concentration of PM10 (μg/m ) Q=Emission source strength per unit length (μg/m-s) H=Average height of the building in the street canyon (m) W=Width of street (m) L=Length of the street (m)

U∥= Wind speed which is in direction parallel to the alignment of street (m/s)

U⊥=Wind speed which is in direction perpendicular to the alignment of street (m/s) I =Characteristic mixing length (m) D=Diffusion coefficient at low wind speeds (m2/s) Characteristic length I can be associated with a typical mixing length caused by turbulent eddies shedding off at roof level and is set to I = 1 m (Mensink et al. 2002). D is the diffusion coefficient at low wind speeds. Diffusion is dominant at low wind speeds. Suggested value of D is 1.5 m2/s (C. Mensink et al. 2006).

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2.2. Site Description Pune is a sprawling city in the western Indian state of Maharashtra at 18°31′13″N 73°5′24″E. Pune-Satara road, is the road connecting bus depot to the Ghat, a pass through the mountains. This road leads to the town of Satara in the southern part of Maharashtra. Due to the increase in traffic on this road in the past years, the Pune Municipal Corporation (PMC) has constructed two flyovers, one from Swargate Bus Terminus to Panchami Hotel and the other from Padmavati to Bharati Vidyapeeth/Katraj. The construction of these two flyovers has considerably reduced the travel time between Swargate to Katraj 7 km route and also the heavy traffic, this highway had been facing over the years.

Figure 1 Location map of Pune-Satara road

2.3. Canyon Aspect The average height (H) of the building is 17.25 meters; length of the street (L) is 200 meters and width (W) of the street was found as 20.6 meters. The aspect ratio H/W for the canyon is 0.84(17.25/20.6) which comes under regular canyon and L/H ratio is 12(200/17.25), hence it is a long canyon (L/H≥7).

2.4. Duration of Study Data for the project was observed from 28th March 2017 to 3rd April 2017. Data was taken twice during the day for an hour. The time duration being: • Morning from 9 a.m. to 10 a.m.(IST) • Evening from 6 p.m. to 7 p.m.(IST)

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2.5. Vehicle Count Vehicular data was obtained by manually counting number of vehicles under each category for the respective duration. The vehicle counting was done by using video recorder placed facing the street. The vehicles are categorized under four types namely: • Two-Wheeler vehicles. • Three-Wheeler vehicles. • Four-Wheeler vehicles. • Heavy Duty vehicles.

Table 1 Models of vehicles under each category TWO THREE WHEELERS FOUR WHEELERS HEAVY DUTY WHEELERS Moped(2-stroke) Three Wheeler(2-stroke) Passenger Cars(Petrol) BS-III HCV Diesel Bus Moped(4-stroke) Three Wheeler(4-stroke) Passenger Cars(Petrol) BS-II HCV CNG Bus Scooter(2-stroke) Three Wheeler CNG OEM (4-stroke) Passenger Cars(Petrol) BS-I HCV Diesel Truck Scooter(4-stroroke) Three Wheeler CNG Retro (2-stroke) Passenger Cars(Diesel) BS-III Motorcycle(2- Three Wheeler LPG (Retrofit 2-stroke) Passenger Cars(Diesel) BS-II stroke) Motorcycle(4- Three Wheeler(Diesel Passenger Cars(Diesel) BS-I stroke) Passenger Cars CNG Passenger Cars LPG MUV Diesel LCV Diesel MUV-Multi Utility Vehicle LCV-Light Commercial Vehicle HCV-Heavy Commercial Vehicle

8000 7000 6000 5000 4000 Two wheeler 3000 Three wheeler 2000 Four wheeler 1000 Heavy Duty 0

Figure 2 Graph of Number of Vehicles from 9a.m.-10a.m.(IST)

Source: Data collected at site

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10000 9000 8000 7000 6000 5000 Two wheeler 4000 Three wheeler 3000 Four wheeler 2000 1000 Heavy Duty 0

Figure 3 Graph of Number of Vehicles from 6 p.m.-7 p.m.(IST)

Source: Data collected at site

2.6. Wind Speed and Direction Wind velocity was measured using Anemometer and wind direction was found using Wind Vane. Two types of wind directions were taken into account. • Perpendicular to street • Parallel to street

2.7. Emission Source Strength In this study, vehicular emissions were calculated using the hourly traffic volume and the emission factors for each category of vehicle. The emission source strength was computed on the basis of emission factor and number of vehicles. It can be calculated as:

-1 -1 -1 -1 Qi (g-km -hr ) = Σ (Ni (hr ) X EFi (gm-km ))

Where

Qi= Emission source strength of vehicle category i

Ni=indicates number of vehicles per hour of category i,

EFi= represents emission factor of PM for corresponding vehicle category. Emission factor is the representative value that attempts to relate the quantity of a pollutant released to the atmosphere with an activity associated with the release of that pollutant. The emission factors of PM10 used are chosen from studies conducted by CPCB (India).

Table 2 Emission factors for different vehicle type CATEGORY OF VEHICLE EMISSION FACTOR Two Wheeler 0.05 Three Wheeler 0.07 Four Wheeler 0.1 Heavy Duty 0.2

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2.8. Background Concentration

The background concentrations for PM10 were abstracted using SAFAR (System of Air Quality and Weather Forecasting and Research) data. The background concentration was derived from the Air Quality Monitoring Station (AQMS) installed by SAFAR in Katraj area. The station Background concentration is denoted by Cb. The station from which the following Cb data is obtained was located at a distance of 580 meters.

2.9. Particulate Matter Concentration Using Street Box Model developed by Mensink and Lewyckyj the final concentrations were obtained for the required duration. Total concentration is denoted by C measured in μg/m3.

Table 3 Final Concentration of PM10 using Street Box Model by Mensink and Lewycky C(μg/m3) at 9:00-10.00 C(μg/m3) at 6.00-7.00 Sr. No. Date a.m.(IST) p.m.(IST) 1 28 March,2017(Tuesday) 163.02 132.64 2 29 March,2017 (Wednesday) 162 130.42 3 30 March,2017(Thursday) 154.94 133.62 4 31 March,2017(Friday) 185.28 141.54 5 1 April,2017(Saturday) 166.51 159.8 6 2 April,2017(Sunday) 161.75 149.65 7 3 April,2017(Monday) 170.68 125.74

3. OUTCOME 3 1. The PM10 concentrations for the week was found to vary between 125-185 μg/m which are exceeding the ambient air quality standard prescribed by CPCB (the standard being 60 μg/m3 for PM10). Hence, the air quality in the area under study with respect to PM10 concentration was found to be poor.

2. When winds are perpendicular to the street the concentrations of PM10 was found to be more than that when they were parallel to the street

3. When the wind speed was found to be higher, the dispersion would be more active and the pollutants dilution will be better, resulting lower levels of PM10. Counter, when the wind speed will be lower it resulted in elevated PM10

4. The concentration levels of PM10 varied directly with respect to traffic density. REFERENCES

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