Delhi: Forecasting systems failed to predict high-pollution episodes, says study

Delhi’s air pollution forecasting systems picked up the pollution trend but failed to predict high-pollution episodes last winter, a case study has found.

Forecasting systems will need to more accurately predict PM2.5 concentration to reduce high pollution episodes during the next winter, the study, conducted by the Council on Energy, Environment and Water, said. The study, however, said the systems helped prevent ‘extremely severe’ pollution episodes since short-term emergency measures were introduced based on the forecasts.

“The first set of restrictions was put in place on 16 November 2021, and all were lifted by 20 December 2021, save the one on industrial operations,” the study said.

“During this period, all the forecasts except AQ-EWS (3-day) (Air Quality Early Warning System) underpredicted PM2.5 levels.”

When the restrictions were imposed, the air quality was still to descend into the ‘severe +’ category. The first ‘severe’ air quality period in December was seen between December 21 to 26. By that time, the Delhi government had lifted all the restrictions, except for those on industrial activities. This was the season’s longest ‘severe’ air quality spell, according to the study. It also said the lifting of the restrictions was “ill-timed”.

Around 64% of Delhi’s pollution load in winter comes from outside the National Capital, the study, which used data from the Decision Support System for Air Quality Management in Delhi, Air Quality Early Warning System for Delhi, and UrbanEmissions.Info, said.

Local PM2.5 sources include dust (7%), transport (12%), and domestic biomass burning (6%).

Tanushree Ganguly, one of the authors of the study, told The Indian Express that the forecasts effectively picked up trends. Where it did not fare well was when it came to predicting the kind of pollution episodes.

There were days when pollution was in the ‘severe’ category, but the forecasts said that it would be in the upper end of the ‘very poor’ category. Ganguly said the underpredicting by the forecasts was the reason behind it.