Updated: September 27, 2021 9:32:59 am
The System of Air Quality and Weather Forecasting And Research (SAFAR) Project under the Ministry of Earth Sciences received a global nod for the first official indigenous framework to forecast air quality in Delhi, Mumbai, Pune and Ahmedabad.
What is the framework developed by SAFAR to forecast air quality in the four metro cities?
The framework was first developed and implemented for Delhi in 2010, used in Pune from 2013 and in 2015 it was extended to Mumbai and in Ahmedabad (2017). SAFAR chose to demonstrate its forecasting model in four different and contrasting micro-climates of Indian cities.
There are six elaborate components in the framework including data from the air quality and weather monitoring stations network, inventory of emissions to keep track of pollution sources, the Air Quality Index among others.
The chaotic nature and complexity of air pollution itself make prediction a challenging task, particularly in a city that is highly influenced by meteorology due to its geographical location, which is considered in this work. We use round-the-clock air quality and weather parameter measurements, scientific analysis to improve forecasting capabilities.
SAFAR framework considers almost all pollutants levels—PM10, 1, 2.5, CO, NOx, SO2, Volatile Organic Compounds etc.— through automatic analysers. With this framework, India will no longer need to depend on any international air quality forecasting frameworks. This is developed as per the country’s micro-climatic conditions. SAFAR’s forecasting model is comparable to the framework by the United States Environmental Protection Agency (US-EPA).
What is the range of the air quality forecast?
We can give air quality forecasts 24, 48 and 72 hours in advance. And in case of extreme pollution events like dust storms or stubble burning issues, we also started extended range forecasts which give forecasts five days in advance. Air quality parameters are very dynamic, they have a very short life. Scientifically, it is not viable to give air quality forecasts more than five days in advance. From 2017, new things were added to it and further indigenisation and outreach aspect was added to the model.
How will air quality forecasting help citizens?
Using this forecasting model, all urban local bodies can issue timely health advisories to alert citizens of bad air days in advance, which will help save vulnerable groups from severe health impacts of air pollution. This framework is a one-stop solution for air quality management leading up to mitigation, and also helps formulate micro-specific air action plans based on robust science. This framework can be easily replicated in 132 cities across the country with a population of over 10 lakh.
How many air quality monitoring stations are required in a city like Mumbai to get accurate forecasts?
Many people say there should be 100 or 200 monitoring stations in a metropolitan city. But I do not agree. It is not practically possible. These instruments are expensive. After the basic requirement of the stations is met, the money can be put into mitigation measures rather than for installing new instruments.
On the other hand, having an inadequate number of stations would give us biased data and not truly represent the city. As per the World Meteorological Organisation research, where I was a representative from India, in a city where the population is between 50 lakh to 1 crore, a minimum of 10 to 12 stations is a requirement. Next is the placement of the stations, which depends on the geography and land cover and use. There are six micro-environments which should be covered, viz traffic junctions, residential, industrial locations, upwind and downwind locations, and in a coastal city like Mumbai, one should be near the coast.
What is emission inventory and its role in air quality forecasting?
An emission inventory is nothing but accounting for all sources of pollutants in a particular area at a particular time. It details the amount and types of air pollutants released into the air and provides information on the types of sources that are emitting the pollutants and their location. It is the most important input in the air quality forecast model. This data is also crucial in policy determination, mitigation planners and micro-level planning.
It is important to understand that emissions in an area are not directly proportional to the air pollution levels in that area.
For example, there is a source of emission in an area (burning of fossil fuels), and the wind direction is in the east. Then a place that is even 3 km away from that source of emission will be highly polluted, whereas an area which is 500 metres in the west from the source will not record a high level of pollutants. The forecasting framework includes all these elements.
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