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Delhi cloud-seeding trial put on hold, says IIT Kanpur. Here's why

The cloud-seeding trial in Delhi scheduled for Wednesday was postponed. Trials held on Tuesday produced no rainfall but led to a 6–10% reduction in PM2.5 and PM10 levels.

Delhi's cloud-seeding trial put on hold
Delhi's cloud-seeding trial put on hold Image Source : PTI
Published: , Updated:
New Delhi:

A planned cloud-seeding trial in Delhi on Wednesday has been put on hold due to insufficient moisture in the atmosphere, the Indian Institute of Technology-Kanpur (IIT-K) said in a statement. The process, which aims to artificially induce rainfall, depends heavily on the right cloud and humidity conditions, officials explained.

Tuesday’s trial showed limited rain 

The Delhi government, in collaboration with IIT-Kanpur, conducted two trials on Tuesday in areas including Burari, North Karol Bagh, Mayur Vihar, and Badli. While no rainfall was recorded in Delhi, light rain was observed in parts of Noida and Greater Noida. Despite minimal precipitation, IIT-Kanpur said the exercise provided important insights for future experiments. Moisture levels during the trials were only 15–20 per cent, which was not enough to trigger rainfall.

Air quality showed measurable improvement 

According to data collected from monitoring stations across Delhi, there was a notable drop in pollution levels after the trials. The PM2.5 and PM10 concentrations fell by 6 to 10 per cent, suggesting that even without rain, cloud-seeding activities may help reduce air pollution in the National Capital Region (NCR).

A Delhi government report released on Tuesday evening confirmed that air quality improved in areas where cloud seeding was carried out.

  • In Mayur Vihar, PM2.5 fell from 221 to 207 and PM10 from 207 to 177.
  • In Karol Bagh, PM2.5 dropped from 230 to 206 and PM10 from 206 to 163.
  • In Burari, PM2.5 declined from 229 to 203 and PM10 from 209 to 177.

IIT-Kanpur said that the trials have helped gather valuable data for improving future operations. “These observations strengthen planning for future operations. Such learnings form the foundation for more effective deployments ahead,” the institute said.

 

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