System of Air Quality Forecasting and Research (SAFAR) is an operational air pollution forecasting service for the citizens of Delhi
SAFAR provided information to the public services and local people about the current level and forecasted (24 hours in advance) the level of air quality at various key locations of Common Wealth Games (CWG) through wireless LCD and LED display panels installed at 20 different locations in Delhi Forest (MoEF) in November 2009. Using these MoEF standards and by fixing the break points and applying the mathematical formulations, scientists have defined the concept of AQI for India
Overall Assessment of Air Quality
The Air quality was found to be of the mixed nature in the above AQI scale but not as bad as speculated by some quarters prior to the games period. Preliminary scientific evaluation of the data was generated from air chemistry transport forecasting model as well as from the dense network of monitoring stations in Delhi
The Air Quality in Delhi
Science Based Assessment of Air Quality in Delhi
Before Games: The relatively low level of PM-2.5 and PM-10 before games could be attributed to prevailing meteorological condition (heavy rains and cloudy conditions) which minimized the windblown dust emissions and also caused washout of PM-10 and PM-2.5. The dust originating from the construction activities could have deteriorated the PM-10 level but excess rain has helped it to settle down quickly. The photochemical formation of ozone was also reduced because of overcast sky conditions in spite of moderately high NO2 level, keeping ozone level in good range. This was quite satisfactory as surface ozone is one of the most toxic gase which is responsible for majority of the respiratory system related diseases like asthma, chest congestion, etc., a well established fact in Delhi as reported by epidemiologist due to the rush in hospital admissions and OPD visits increase when high ozone emission are noticed.
During Games: The AQI was found to be mostly in poor range for Particulate Matters and in moderate range for ozone. The increase in the level of Particulate Matters, especially PM10 during the games period as well as ozone was due to the clear sky and stagnant wind conditions, which lead to accumulation of Particular Matter in the boundary layer and more photochemical ozone formation. Moreover, due to the dry soil with sunny days, the emission of coarser particles, specially PM-10 has increased significantly through windblown dust from paved and unpaved roads supported by fast moving traffic but contribution of fossil emissions was in check (up to certain extent) due to traffic restrictions as a result PM-2.5 level were found relatively better. Air quality scenario showed relatively poorer air quality at Major Dhyanchand National Stadium, Talkatora Garden
It was interesting to note that PM-10 and PM-2.5 suddenly became very high in the night time (between 12 midnight to 3AM) at several venue sites which initially could not be forecasted by the model because it was suspected to be due to the artificial fogging (for insects and mosquitoes) of the area surrounding the stadiums, a purely localized effect which disappeared after the games. The regular fogging during the games at night showed unusual impact on the air quality at night.
After Games: The level of particulate matters mainly PM-10 further increased and is more or less in the upper limit of poor range during day time probably due to traffic rush hours. Traffic did increase the fossil fuel combustion but it appears that it has more severely affected the windblown dust emissions from paved and unpaved road because the magnitude of PM-10 level increased significantly than that of PM-2.5.
Test of Forecasting Ability of Safar
Most of the time the forecasting capabilities are restricted due to lack of high resolution emission inventories of the pollutants. Hence for the air quality forecasting during CWG -2010, for the first time, a high resolution gridded emission inventory for 2009-2010 was developed and released just before the games. The accuracy and reliability of these inventories were validated using air quality forecasting model and its validation through monitoring network. The model validation provided a reasonably satisfying result well within the acceptable error for gaseous pollutants baring a few unexpected localized exceptions like the fuming impact in stadium during nights.
In case of Particulate Matters, when the emission inventory was used without accounting for windblown dust from paved and unpaved road and construction activities, the forecasting model greatly underestimated the observations mainly the PM-10 level. It was not used initially because it involves high amount of uncertainty and is hardly considered as significant source earlier by any modeler. However, scientists prepared it by collecting the relevant activity data but released separately as supplement to the original report because of its highly uncertain nature where the magnitude of its emissions was calculated to be very close to the sum of all other sector including transport, industry, bio-fuel, etc. However, when forecasting model accounted for this sector, the results of Particulate Matter were found to reproduce the observed data reasonably well. The air quality forecast from SAFAR system is found to be within 10% to 20% confidence limit of observations. Forecast model is also able to resolve the diurnal pattern quite satisfactorily.
Through this sensitivity test, it was seen that unnoticed sectors of windblown dust from paved and unpaved roads and construction activities is one of the major contributors in PM and some time even stronger than fossil fuel combustion from transport sector and hence need attention. The calm winds, temperature inversion and formation of relatively stable and persistent boundary layer during and after the CWG games played a vital role in distributing the level of air pollution and kept the dominance of localized effect and minimized the effect of transport from neighbouring states.
In addition to emissions, the air quality forecasting is highly influenced by meteorology and hence the SAFAR system first validated weather parameters obtained from the model with that of Automatic Weather Stations (AWS) monitoring network of India Meteorology Department and Indian Institute of Tropical Meteorology, Pune prior to air quality forecast. Hence, the air pollution problem and its forecasting services would be best served by simultaneously dealing with meteorological parameters and its forecast rather than dealing in isolation for planning of scientifically tenable mitigation strategies.
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