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A number of policy measures have been activated in India in order to control the levels of air pollutants such as particulate matter, sulphur dioxide (SO(2)) and nitrogen dioxide (NO(2)). Delhi, which is one of the most polluted cities in the world, is also going through the implementation phase of the control policies. Ambient air quality data monitored during 2000 to 2003, at 10 sites in Delhi, were analyzed to assess the impact of implementation of these measures, specifically fuel change in vehicles. This paper presents the impact of policy measures on ambient air quality levels and also the source apportionment. CO and NO(2) concentration levels in ambient air are found to be associated with the mobile sources. The temporal variation of air quality data shows the significant effect of shift to CNG (Compressed Natural Gas) in vehicles.

In this study, an artificial neural network is employed to predict the concentration of ambient respirable particulate matter (PM 10 ) and toxic metals observed in the city of Jaipur, India. A feed-forward network with a back-propagation learning algorithm is used to train the neural network the behavior of the data patterns. The meteorological variables of wind speed, wind direction, relative humidity, temperature, and time are taken as input to the network. The results indicate that the network is able to predict concentrations of PM 10 and toxic metals quite accurately. INTRODUCTIONRecently, artificial-neural-network-based prediction techniques have become more popular because of their ability to generalize the nonlinear patterns in data sets. Their most important advantage is that they can solve problems that are too complex for conventional technologies such as statistical methods. These problems include pattern recognition and forecasting. Their applicability is increasing in airquality predictions because of their ability to handle uncertainties and complex relationships in the data.

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