Aerosol and Air Quality Research, 13: 748–755, 2013 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2012.02.0044

Spatio-Temporal Variation and Deposition of Fine and Coarse Particles during the Commonwealth Games in Delhi Ali Kaushar, Dilip Chate*, Gufran Beig, Reka Srinivas, Neha Parkhi, Trupti Satpute, Saroj Sahu, Sachin Ghude, Santosh Kulkarni, Divya Surendran, Hanumant Trimbake, Dinesh Kumar Trivedi Indian Institute of Tropical Meteorology, Pune - 411 008, India ABSTRACT

The Indian Government implemented the project “System of Air quality Forecasting And Research (SAFAR)” for the “Commonwealth Games” 2010 in Delhi. It was adopted by the Global Urban Research Meteorology and Environment of World Meteorological Organization as its pilot project. We used data from a dense network of stations built over 2500 km2 in Delhi under the SAFAR project to investigate temporal and spatial variations of fine (PM2.5) and coarse (PM10–2.5) particles, and discuss their deposition and the airborne mass fractions that were retained after a certain amount of time. The 24-hour coarse particle (PM10–2.5) means during the Games period were always above the National Ambient Air Quality Standard NAAQS (100 μg/m3) at all the sites except the airport. In still air, initial PM10–2.5 can reach below 50 μg/m3 by deposition in an hour. The 24-hour PM2.5 means reveal that they were either around or below the NAAQS (60 μg/m3) at some sport complexes, whereas they fluctuated between 60 and 80 μg/m3 at the other sites. Keywords: Air-quality; Aerosol deposition; PM2.5 and PM10–2.5; Pollutants source; Vehicular emissions; CWG-2010. INTRODUCTION

Adequate understanding of spatio-temporal variability of

particulate matters (coarse and fine particles) and their sink mechanisms is needed to know their impact in health hazards and climate change (Colbeck et al., 2011, Tsai et al., 2011, Gugamsetty et al., 2012). Gugamsetty et al. (2012) have studied source apportionment of the particulate matters at Shinjung station in New Taipei City, Taiwan. Based on the chemical information, they identified five source types viz., soil dust, vehicle emissions, sea salt, industrial emissions and secondary aerosols. Tsai et al. (2011) have studied physicochemical properties of particulate matters at Inland and Offshore sites over South-eastern Coastal Region of Taiwan Strait. They have found that the most abundant ionic species of PM were SO4

2–, NO3–, and NH4

+ and the most common chemical compounds were ammonium sulfate ((NH4)2SO4) and ammonium nitrate (NH4NO3). On the other hand, in Lahore, Pakistan, Colbeck et al. (2011) have shown that coarse (PM10–2.5) and fine (PM2.5) particles was significantly high on road side mainly due to re-suspension of dust and from automobile exhausts. * Corresponding author. Tel.: 00912025904257;

Fax: 00912025865142 E-mail address: chate@tropmet.res.in

Delhi is the seventh most populous megacity in the world, where more than 100,000 petrol and diesel consuming vehicles add annually to the roads (Department of Transport, Delhi, 2007). After the Summer Olympic Games-2008 in Beijing, China (Wang et al., 2010), the Commonwealth Games (CWG-2010) was one of the biggest recent sports event in Delhi, India. During the CWG-2010, many international athletes joined the sports events and the air quality was a major concern to which several efforts were taken up by the Indian Government and researchers. The regulatory authorities of the government took strict actions for the reduction of emissions of air pollutants from industry, road traffic, and construction sites. New compressed Natural Gas (CNG) buses and radio taxis were fleeted in Delhi. Heavy vehicles were banned from entering Delhi, and separate lanes were dedicated for the athletes to cut down on vehicular emissions along those corridors. New metro lines were introduced for travellers of sports complexes. Coal based power plants were closed down and additional natural gas was consumed at the power plants. With the timely implementation of aforementioned norms, the Delhi’s air quality could be improved during the Games period.

These measures for clean air during the Games provide a rare opportunity for assessment of the impact of pollution emissions on the air quality of the National Capital Region (NCR) of Delhi. The current levels of air pollutants and predicting air quality well in advance is required for knowing the immediate health hazard in any city. A sustainable

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program of air quality monitoring and forecasting system, the network of Air Quality Monitoring Stations (AQMS) with Automatic Weather Station (AWS) under ''SYSTEM OF AIR QUALITY FORECASTING AND RESEARCH'' (SAFAR), was built exclusively for CWG-2010 covering Delhi-NCR (http://safar.tropmet.res.in/). For air quality forecasting, a high resolution (1.67 km × 1.67 km) emission inventory of major air pollutants was developed for a domain of ~70 km × 65 km (~4500 km2) covering Delhi and its surrounding regions (Sahu et al., 2011). Also, the breakpoints classification (as the best estimations under the given constraints) has been developed for different pollutants by Beig and Ghude, (2010) for reporting the Air Quality Index (AQI) of Delhi during CWG-2010 (http://safar.tropmet.res.in/). Altogether, the system has displayed both observed and forecasted levels (24 hours in advance) of air quality at various key locations of Delhi on wireless LCD and LED display boards in terms of the AQI during CWG-2010. Further analysis of PM2.5 and PM10 data and influence of wind speed, temperature and relative humidity on variations in fine and coarse particles is presented in this paper.

The difference between integrated mass of aerosols up to aerodynamic diameter 10 μm (PM10) and up to 2.5 μm (PM2.5) represents coarse fraction (PM10–2.5). Statistical analysis of air pollution data for coarse (PM10–2.5) and fine (PM2.5) particles and meteorological parameters across a network of AQMS and AWS under SAFAR during Games period (CWG-2010) can serve a long term strategic vision for improvement of air quality in Delhi. With the purpose, the objectives of the present study are to project time series of

PM2.5 and PM10–2.5 mass concentration during CWG-2010 from the study area under SAFAR and to analyze links between these particles and wind, temperature and relative humidity in Delhi. With deposition velocities of fine and coarse particles, airborne mass fractions of these particles retained after the elapse time are projected at breathing height as a case study. Study Area

The study area is AQMS and AWS network of SAFAR in Delhi-NCR (Commonwealth Games, CWG-2010) comprising of sports complexes, airport and residential sites as shown in Fig. 1. It may be noted in the figure that IITM, Delhi (1) is residential area, Palam-IGI (9) is airport site and Yamuna Sports Complex (YSC) (2), Indira Gandhi Sports Complex, IGSC (3), Major Dhyan Chand National Stadium, MDNS (4), Thyagaraj Sports Complex (6), Common Wealth Game Village, CWGV (7), University of Delhi, DU (educational cum sports site) (8) and Talkatora Garden, NDMC (10) are sports complexes; hereafter they will be referred as (1), (2), (3), (4), (6), (7), (8), (9) and (10). MEASUREMENT TECHNIQUE AND DATA ANALYSIS

The present study is based on measurements of the concentration of atmospheric aerosols with cutoff aerodynamic diameters up to 10 μm (PM10) and 2.5 μm (PM2.5); and meteorological parameters like wind speed, air temperature and relative humidity.

Fig. 1. Area shows Network setup under SAFAR for atmospheric measurements.

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PM10 and PM2.5 were continuously monitored using Beta Attenuation Monitor (BAM-1020; Met One Instruments, Inc, USA) which uses the industry-proven principle of beta ray attenuation. The measurement principle involves emission, by a small 14C (carbon-14) element, of a constant source of high-energy electrons known as beta rays through a spot of clean glass fiber filter tape. These beta rays are detected and counted by a sensitive scintillation counter to determine a zero reading. The BAM-1020 automatically advances this spot of tape to the sample nozzle, where a vacuum pump then pulls a measured and controlled amount of dust-laden air through the filter tape loading it with ambient dust. This dirty spot is placed back between the beta source and the detector thereby causing an attenuation of the beta ray signal which is used to determine the mass of the particulate matter on the filter tape and the volumetric concentration of particulate matter in the ambient air. The instrument measures concentration of ambient aerosols with a resolution of 0.1 μg/m3 and lower detection limit of around 1 μg/m3. Span check of the instrument is automatic and is verified hourly (Kindly see BAM-1020 Operation Manual for more details).

Wind speed, air temperature and relative humidity were measured using an AWS system (Model, ME-1310;

Microcomm-ESD, UK). The temperature sensor used has resolution of 1°F with accuracy ± 1°F. Relative humidity (RH) sensor used with this system has accuracy ± 3%. The wind sensor measures wind speed in the range 0–56 m/s with an accuracy of ± 0.45 m/s and resolution better than 0.045 m/s. Data from all the measurement sites in and around Delhi city were stored in computerized data acquisition system located at a centralized hub (IITM, Delhi). RESULTS Time Series of PM10–2.5 and PM2.5 during Commonwealth Game Period

Daily average values of PM10–2.5 and PM2.5 mass concentrations based on their observations in the Delhi-NCR region during 23 September–24 October, 2010 are presented in Fig. 2. The above period includes Commonwealth Games period (3–14 October 2010; from day number 276 to 287 in the figure) also. The particulate data for the locations 2 and 3 were not available for the periods except that for the CWG-10 period and so these two plots contain daily average concentrations only for the CWG-10 period. It may be seen in the figure that the mean coarse particles (PM10–2.5) mass concentrations was higher than fine (PM2.5) particles on

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Day number of a year Fig. 2. Daily average concentration of aerosols during 23 September–24 October 2010 at different locations in Delhi. Note: The above period includes the Commonwealth Games period (3–14 October 2010; from day number 276 to 287) at Delhi.

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opening day of CWG-2010 (3rd October) over the locations (2), (3), (4), (6), (7), (8) and during 8/9th to 14th October, 2010 (i.e., during day number 281/282 to 287) over the locations (2), (3), (4), (6), (7), (8) and (10). The mass of coarse particles showed always higher concentration than that of fine particles at location (4). This result is attributed to vehicle-driven roadside dust and wind-blown dust which mostly remains in the lower layer of the atmosphere. On the other hand, at location (9), mass of fine particles showed always higher values. This is mainly because of the emission of pollutants from the airplanes and light vehicles and less suspension of soil dust due to less heavy vehicles activity in this outer area of Delhi as the location (9) is an airport location much away from the main city. Another important feature seen in the figure is the occurrence of higher concentration of PM2.5 than PM10–2.5 during 3–9 October (i.e., during day number 276–282) at locations 1 and 10. The reason lies in the fact that both these locations are situated in the areas where soil oriented dust suspension in the boundary layer is normally less than those at many other locations due to the areas mostly being covered by concrete, tar or plants. Location 1 is a forest cover area where plants may reduce coarse particles suspension by intercepting them. Thus emission of fine particles and gases from the vehicles and conversion of these gases to particles by gas-to-particle conversion mechanism may be the main reason for more concentration of PM2.5 than PM10–2.5 at these locations.

National Ambient Air Quality (NAAQ) Standards using Beta attenuation system for continuous monitoring of PM2.5 and PM10 are set to 60 μg/m3 and 100 μg/m3 for 24-hour average respectively (http://cpcb.nic.in/National_Ambient_ Air_Quality_Standards.php). It is seen in Fig. 2 that daily average of PM2.5 concentration were around or even below their NAAQ standard (60 μg/m3) during 10th to 12th October, 2010 (i.e., during day number 283 to 285), at nearly all the locations except at (8) and (9) where the concentration remained nearly always higher than NAAQ standard. High emissions of fine particles from the transport sector nearby Delhi University (8) and airport premises (9) could be responsible for levels of fine particles above their NAAQ standard. It is found that, on spatial average, the PM2.5 concentration was below NAAQ standard on ~9% occasions and at or below 80 μg/m3 on about 27% occasions during CWG-10. It is location 4 where, on spatial average, frequency of occurrence of less concentration than NAAQ standard is maximum (~5%). The coarse particles (PM10–2.5) concentration crossed their NAAQ standard (100 μg/m3) during Games period for all locations except for airport site (9). At airport site, the PM10–2.5 concentration remained below NAAQ standard for most of the time. The concentrations at locations 1 and 10 moved around the NAAQ standard (i.e., around 100 μg/m3). But at remaining locations, except at location 9, the concentration of coarse particles remained much higher for most of the observational period.

A remarkable point noticed in the figure is that particulate concentration during the period (from day number 266 to 275) before the Commonwealth Games is a little less than that during the Games period. This is due to washout of the particulate matters by continuous rain of the monsoon

season. During the period after the Games, coarse particles show nearly same concentration as during the CWG but the fine particles show escalated concentration level after the Games. The average concentration of the fine particles before the CWG, during the CWG and after the CWG is ~85, 113 and 117 μg/m3. It means that the fine particulates concentration had increased by 4 μg/m3 immediately after the CWG. This increase is attributed mainly to vehicular and industrial emissions. Tiwari et al. (2012) have reported that, during the period from day number 276 to 287 of the year 2007, the minimum and the maximum concentration of PM2.5 in Delhi were ~100 and ~300 μg/m3 and of PM10–2.5 were 125 and 200 μg/m3 respectively. On the other hand the present minimum and maximum concentration of PM2.5 on average were ~62 and 178 μg/m3 and of PM10–2.5 were ~31 and ~77 μg/m3

respectively. There are a few other publications which also show that the fine and the coarse particulates concentrations in Delhi are much larger than those reported during the Commonwealth Games period (Tiwari et al., 2009; Perrino et al., 2011; Tiwari et al., 2012). The above comparison and the other scientific works on the subject confirm that the control measures taken by the Government of India to reduce the emission during the Commonwealth Games period were effective. Influence of Meteorological Parameters on PM10–2.5 and PM2.5

Fig. 3 presents relationship of ambient aerosols (PM10–2.5 and PM2.5) with wind speed, temperature and relative humidity based on their regression analysis. It may be noted here that while regressing the fine and the coarse particles concentration on wind speed, temperature and relative humidity we have considered spatial average values of these parameters (averaged over 9 data sets of AQMS and AWS in the Delhi-NCR during games period). This is because of the uniformity in the prevailing weather condition over the whole region of Delhi-NCR during October.

It is well documented that the region of Delhi-NCR is located far inland off the major water reservoirs like the Bay of Bengal and the Arabian Sea. After monsoon season (June–September), the general weather in Delhi is characterized by hot days with low humidity and cool nights with appreciable humidity. The condition favors lowering of inversion layer during this month. The climate of this area clearly shows the influence of its inland position and the air over this region is mostly dry. Winds during October are generally light and predominantly from westerly/northwesterly direction and tend to northerly in the afternoon (India Meteorological Department (IMD), 1991). Thus there is little chance of spatial variation in the values of meteorological parameters in the Delhi region. The only weather phenomena which influences this persisting weather condition and causing rainfall in the region during October is the Western Disturbances (WD). But this also covers the whole region of Delhi and the meteorological condition over the Delhi region during October again remains uniform. Though not shown for brevity, the present weather data also verifies existence of uniform scenario of the weather parameters in this region.

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(g/m3) = 6.8 RH (%) - 293.8

Relative humidty (%) Fig. 3. Relationship of PM10–2.5 and PM2.5 with meteorological parameters during Commonwealth Games (3–14 October 2012) at Delhi.

The PM10–2.5 shows statistically insignificant correlation with wind speed (r = 0.24), temperature (r = –0.24) and relative humidity (r = 0.1) at 5% level of significance. Although insignificant, but positive relationship of the coarse aerosols with wind speed implies that these aerosols drifted very slowly in the average winds recorded over the region, but they decreased with the increase in ambient temperature. No any concrete conclusion can be drawn based on these relationships as they are statistically insignificant. As indicated in Fig. 3, PM2.5 particles show highly significant negative relationship with wind speed with correlation coefficient of –0.85 (statistically significant at 0.1%). This result is attributed to the fact that when there is low wind fine particles tend to remain over the region of origin and when there is high wind they are drifted vertically as well as horizontally through turbulent transfer mechanism causing reduction in their concentration in the surface layer of the atmosphere. On the other hand, PM2.5 shows positive relationship with air temperature (r = 0.43, result not significant at 5%) and relative humidity (r = 0.92, significant at better than 0.1%). Insignificant but fair positive relationship of PM2.5 with temperature can be interpreted as, though higher temperature has favored gas-to-particle conversion mechanism, this has not been the

important factor impacting on this mechanism. The important factors for the occurrence of fine particles in the surface layer of Delhi region has been lowering of inversion layer and particles left behind after the dispersion of fog. This interpretation comes from highly significant relationship of the fine aerosols with the relative humidity. Actually, lowering of inversion layer which normally occurs during this month causes trapping of the fine particles emitted directly from anthropogenic activities or formed by gas-to-particle conversion mechanism. These particles act as cloud condensation nuclei (CCN) in the boundary layer of the atmosphere causing fog formation over the region. When sun rises, fog is dispersed leaving the fine mode particles suspended in the lower layer of the atmosphere. Deposition Velocity and Dry Deposition Flux of Aerosol Particles

In still air, gravitational forces on large particles govern the air quality by virtue of their significant dry depositions to the surface in absence of rain. We prefer to discuss dry deposition of PM10–2.5 and PM2.5 particles considering terminal fall rates and relaxation times during their downward motion in still air, as no data of three components of winds with high resolution (0.05 to 0.1 s) are available during games

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period. Depending on their size and mass, particles in the atmosphere are affected to different degrees by gravitational forces. In the absence of significant convection, particles (particularly large particles) move toward the surface of the earth by gravity. The terminal rate of fall of heavy particles (> 2 μm) in still air, due to gravity (Seinfeld and Pandis, 2006) is vs = g (1) where g is the acceleration due to gravity and is a relaxation time for a moving particle in air.

For particles which follow Stokes law of resistance,

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18p pD

(2)

where, ρp is the density of the particle, Dp is the diameter of particle and η is the viscosity of air. Particles of size larger than 2 μm (PM10–2.5) tend to have a deposition

velocity (vs) as high as ~0.003 m/s over land surface. Deposition velocity for particles in the size range 0.1–2.5 μm (PM2.5) is in the order of 0.0001 m/s. Over land surface in Delhi, deposition velocity of fine particles (PM2.5) is assumed to be 0.0001 m/s (Nicholson, 1988) and for coarse ones (PM10–2.5) it is 0.0015 m/s (average vs over the particle sizes from 2.5 to 10 μm) to estimate deposition fluxes and airborne mass after elapse of time at breathing height (Cotton and Levin, 2009).

We have considered the case of observed aerosol mass concentration at the sports complex sites YSC (2) and IGSC (3) for the estimation of deposition flux of the aerosol masses. The wind speeds at these sites are below the detection limit of the AWS sensors. For typical initial mass concentrations of fine and coarse particles at the observational sites (2) and (3), deposition fluxes are plotted in Fig. 4. The deposition fluxes for coarse particles are as high as ten times (~0.25 μg/m2/s) over to those for fine particles (< 0.025 μg/m2/s) at both the sites (2) and (3). Gravitational forces are opposed by a frictional force that is a function of air

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viscosity, particle velocity and particle diameter. As a result, larger particles settle more quickly than smaller ones. As particle size decreases, the effect of gravity is reduced and fine particles settle out slowly. In still air, dry deposition processes can be responsible for rapid depletion of coarser particles in absence of their emissions sources nearby the measurement sites.

Under the stable atmospheric conditions, the mass concentration (m) of aerosols remaining airborne from the initial mass concentration m0, at height h after time t can be expressed as non-dimensional exponential function of vs, elapsed time (t) and breathing height (h),

0 exp sv tm m

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Airborne mass concentration of fine (PM2.5) and coarse

(PM10–2.5) aerosols from their typical initial average mass concentrations at site IGSC (3) in Delhi are computed

using Eq. (3) at height h after time t has elapsed with the assumed terminal fall rates for these particles. The mass concentrations of fine and coarse particles remaining airborne at breathing height h (2 m) after one hour are shown in Fig. 5. It is worth to note that mass concentrations of fine particles deplete in small amount and mostly they (accumulated size range particles) remain airborne after one hour, whereas coarser particles settle gravitationally to the surface in large amount after elapsed time of one hour (Fig. 5). The airborne mass concentrations of PM10–2.5 and PM2.5 as seen in Fig. 5 hold for conditions of still air and no major pollutant sources nearby to measurement sites. The depleted mass concentrations of fine and coarse particles shown in the figure can replenish within no time in case of continuous supply of these aerosols from emissions through various natural and anthropogenic sources or advection of these particles via strong airflows in downwind in the source-receptor system. The levels of PM10–2.5 and PM2.5 presented in this work may not be raised from a single sector.

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CONCLUSION

The measurements of coarse and fine particles across the nine monitoring sites under SAFAR program clearly demonstrate the impact of local sources, meteorology and deposition processes on the time series variations of PM10–2.5 and PM2.5 during the CWG-2010 in Delhi. During the Commonwealth Games, because of the pollution control policies implemented in and around the city of Delhi, the level of coarse (PM10–2.5) and fine (PM2.5) particles plunged to those close by or even below their NAAQ standards. The pollution reduction measures for the Delhi’s CWG-2010 (well before the Games) and washout of air pollutants by monsoonal rain till end of the September, 2010 were effective in reducing atmospheric concentrations of both coarser and fine particles. Much improved air quality on most of the days especially along the dedicated lanes during the CWG-2010 also had apparent relationship with weather changes (monsoon to post-monsoon transition period for CWG-2010). Further air pollution modeling studies using SAFAR network data on important trace gases and aerosol pollutants are needed to quantify the relative role of the emission reduction initiatives and weather changes and the contribution of local versus regional sources to the air quality changes in Delhi-NCR. ACKNOWLEDGEMENT

Indian Institute of Tropical Meteorology (IITM), Pune is supported by the Ministry of Earth Sciences (MoES), Government of India, New Delhi. Authors sincerely acknowledge the whole hearted support of Prof. B.N. Goswami, Director IITM, Pune. Authors appreciate the efforts of entire team involved in SAFAR project under the MoES. REFERENCES Beig, G. and Ghude, S.D. (2010). Scientific Evolution of

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Received for review, February 23, 2012 Accepted, August 20, 2012