Direction: Read the passage given below and answer the following questions. 

According to a report by Greenpeace, 22 out of 30 world’s most-polluted cities are in India. As per recommendations, India requires a minimum of 4,000 air monitoring stations to check air quality. Currently, there are approximately 160 air monitoring stations in India. This implies we have less than 5 percent of the recommended number of air monitoring stations. This inadequacy is a hindrance to policymaking, harming the potential to generate solutions. A base dataset is needed for everything from city planning to new drug development. Air quality monitoring forms the base layer for a number of things, including human health. Like Peter Drucker said: “If you cannot measure it, you cannot solve it”. AI can help solve this problem.

Accurate computation of air quality not only requires combining existing air monitoring infrastructure with satellite data but also factoring in human activities such as traffic, construction, garbage burning, industrial source apportionment, and population density. A feature engineered AI can be designed to utilise the above factors to accurately compute a geospatial interpolation of air quality data. The advantage of such an approach is that it provides a distributed coverage of high spatiotemporal resolution in near real time.

In Delhi, the number of air monitoring stations has increased in recent years. This has provided us with researchbacked sources of air pollution within a few parts of the capital. By understanding the sources of pollution, AI can be further used to track and predict the growth and reduction of air pollution. For example, we could monitor whether an increase in industrial production is directly proportional to air pollution, or a decrease in vehicles is related to a reduction. These decisions could be evaluated by AI, allowing appropriate actions to be initiated. AI can also help in modelling the chemical reactions between pollutants. Algorithms like Atmospheric Transport Modelling System (ATMoS) helps understand PM2.5 concentrations. Additionally, there are advanced algorithms hat help in understanding and predicting smog, haze, visibility, and observe meteorological interventions as well as manage air quality better. That help in understanding and predicting smog, haze, visibility, and observe meteorological interventions as well as manage air quality better.