Table 1: Directional profile of PM10 for summer 2008-2010

Figure 2: Simulation Domains classified as (A) NorthIndia, (B) India, (C) Asia and (D) Large domain (Asia,Africa and Australia).

Table 2: Maximum level of PM10 obtained and percentage contribution of geographical region (max. observed PM10 value is 1134.5 µg m-3)

Figure 1: Simulated Wind and Pollution Roses for (A) 2-3 May, (B) 4-6 May and (C) 1-4 June 2008-2010 at Delhi

Wind

Direction

PM10 Concentration (the no. shows the

max. avg. value achieved ) (µg m-3)Regions located in the specified

direction2008 2009 2010

E ≤ 25 ≤ 25 ≤ 25 Nepal, China

ESE ≤ 25 ≤ 25 ≤ 25 Bangladesh, Burma, Thailand

ENE ≤ 25 ≤ 25 ≤ 25 China

NW ≤ 75 ≤ 25 ≤ 75 Pakistan, Afghanistan, Russia

W ≤ 300 ≤ 300 ≤ 400 Pakistan, Iran, Saudi Arabia

WNW ≤ 400 ≤ 300 ≤ 300 Pakistan, Afghanistan, Iran

WSW 25-200 ≤ 25 ≤ 50 Pakistan, Saudi Arabia

4. Conclusions

•Based on the simulations for different synoptic conditions during selected summer periods in May-June, 2008-

10, it was observed that high concentrations of PM10 were found to be associated with winds coming from

WNW and West directions. The hourly averaged values of PM10 ranges approximately from 25-400 µg m-3.

PM10 concentrations are lower in cases of winds coming from East direction as compared to winds from West

direction.

• The influence of geographical domain on PM10 based on model simulations revealed that the contributions

due to long range transport outside Indian domain can be as high as 40%. It can be inferred that the high levels

of PM10 concentration is not only due to local pollution but is also highly influenced by remote sources. This

however, shows the contribution to PM10 concentrations from Multiscale Transport.

• It is suggested to perform such simulation studies for other seasons as well as other synoptic conditions, so

that factors that influence PM10 concentrations can be understood.

• In megacity Delhi, particulate matter levels remain persistently high. The regulatory measures should

therefore give due importance to regional and long range transport in policy interventions.

Assessment of Contribution to PM10 Concentrations from Multiscale Transport of Pollutants Using WRF/Chem for A Tropical Urban Airshed

Manju Mohan and Medhavi GuptaCentre for Atmospheric Sciences, Indian Institute of Technology Delhi, India

email: mmohan6@hotmail.com

(ii) Influence of different geographical domains on PM10 concentrations

• Table 2 shows the percentage contribution to maximum PM10 concentration simulated due to varying

geographical domain for 3rd June 2010, when highest PM10 value was observed i.e. 1134.5 µg m-3. The

geographical domains are shown in Figure 2.

• Figure 3 represents annually averaged global emission for PM10 for the year 2008.

• In Table 2, the maximum PM10 values achieved for varying domains i.e. Large domain (which includes Asia,

Africa and Australia), Asia, India and North India are shown. Since there is not much difference in the

maximum values of Large and Asia domain it can be said that there is not much influence due to Africa,

Australia and surrounding regions PM10.

• It can be inferred that the high levels of PM10 concentration is not only due to local pollution but is also

highly influenced by remote sources. This however, shows the contribution to PM10 concentrations from

Multiscale Transport. About 60% concentration is due to Indian domain.

• Table 3 gives the statistical analysis of PM10 representing the influence of varying geographical domain for

the simulation leading to maximum concentration in 2010 and shows a reasonable model performance.

3. Results and Discussion

(i) Influence of Synoptic conditions on PM10 concentrations for year 2008-2010

summer season

• 3 periods during summer of 3 years (2008-10) are selected which broadly covered 7

prominent wind directions. Table 1 shows the regions associated to these directions while

Figure 1 depicts wind rose and corresponding pollution rose for these 9 cases. The emissions

are maintained at 2008 level in all cases to extract the impact of only the met. It is inferred

that high concentrations of PM10 are associated with winds coming from WNW and West

direction. PM10 concentrations are lower in cases of winds coming from East direction as

compared to winds from West direction.

1. Introduction and Objective

• The analysis of PM10 with respect to National Air Quality Standards (NAAQS) in India during 2009 revealed that

24-hourly average concentrations exceed standard (100 μg m-3) at 85% of the stations under consideration.

• It is expected that variation of PM10 is not only controlled by the emission rate in PM10 sources at local level alone,

but also by the long range transport as also the met. conditions, dust storms etc. that prevails over Delhi.

• Suitable regulatory mechanisms should include the contributions of various sources and their emissions impacting

the receptors in a given region of interest.

• The main objective of this study is to understand the impact of different geographical domains on PM10

concentration and thereby assess contributions due to multiscale transport over a highly polluted tropical urban

airshed viz; megacity Delhi with due consideration to influences of varying synoptic conditions in the region.

2. Modelling Tools and Simulation Details

• Model: WRF/Chem Version 3.2

• Domains: Three domains of resolution (90km, 30km, 10km)

• Physics option: Lin et al (1983) microphysics, Kain Fritsch

cumulus parameterization, Dudhia shortwave radiation scheme,

RRTM longwave radiation scheme and MM5 Similarity surface

layer- Noah LSM chosen based on sensitivity studies performed

over Delhi (Mohan and Bhati, 2011)

• Chemistry option: RACM gas phase mechanism and

MADE/SORGAM aerosol module

Figure 3: PM10 emissions for 2008 (tons/year). Source: EDGAR

Statistical Parameter

Large Asia IndiaNorth India

Reported values from other studies(Reference)

MNGE (%) 53 50 60 67 <=56 (Zhang et al., 2006)FB (%) -72 -59 -83 -95 ±60 (Spak and Holloway, 2009)

R 0.8 0.77 0.76 0.75 0.57 to 0.92 (Spak and Holloway, 2009)MNB (%) -38 -29 -42 -45 -38 to 35(Zhang et al., 2006)

Table 3: Statistical Parameters evaluation for PM10

References• Mohan, M. and S. Bhati (2011), Analysis of WRF Model Performance over

Subtropical Region of Delhi, India, Advances in Meteorology 2011, 13

• Spak, S. N. and T. Holloway (2009), Seasonality of speciated aerosol transport

over the Great Lakes region, Journal of Geophysical Research 114

• Zhang, Y., P. Liu, A. Queen, C. Misenis, B. Pun, C. Seigneur and S. Wu

(2006), A comprehensive performance evaluation of MM5-CMAQ for the

Summer 1999 Southern Oxidants Study episode—Part II: Gas and aerosol

predictions, Atmospheric Environment 40, 4839–4855

Parent Domain

Maximum

Simulated PM10

(µg m-3)

Percentage

Contribution

(%)

Large (Asia, Africa and Australia) 1041.6 100

Asia 1011 97

India 653.4 62.7

North India 472.3 45.3

Global Atmospheric Watch (GAW) 2013 Symposium and Task Force on

Hemispheric Transport of Air Pollution March 18-22, 2013; WMO, Geneva