On Tuesday, Delhi’s environment minister, Manjinder Singh Sirsa, shared an unsigned report from IIT Kanpur that claimed the city’s recent cloud seeding experiment had been somewhat successful. According to the report, rainfall data and reductions in particulate matter at three locations suggested a measurable improvement in air quality. Yet, experts and data analysts have pointed out serious methodological flaws that render the conclusions scientifically questionable.
What the IIT-K Report Claimed
The report stated:
“The PM2.5 was 221, 230, and 229 reported from Mayur Vihar, Carol (Karol) Bagh, and Burari, respectively, before cloud seeding, which got reduced to 207, 206, and 203, respectively, after the first seeding. Similarly, PM10 was 207, 206, 209, which got reduced to 177, 163, 177 at Mayur Vihar, Carol Bagh, and Burari, respectively. Given that winds were negligible, one possible explanation is that the denser moisture content created due to seeding particles has helped in settling down a portion of these particles, which translated to these reductions.”
At first glance, these numbers suggest modest improvements. But a closer examination shows the report lacks basic scientific rigor, making it impossible to conclude anything about cloud seeding’s effectiveness.
1. Missing Units
The report does not specify the units of measurement for PM2.5 and PM10. These refer to airborne particles smaller than 2.5 micrometres and 10 micrometres, respectively. Standard practice is to report their concentration in micrograms per cubic metre (µg/m³).
India’s National Ambient Air Quality Standards (NAAQS), for instance, define PM2.5 limits as 60 µg/m³ (24-hour average) and PM10 limits as 100 µg/m³ (24-hour average). Without units, it is unclear whether the report is referencing raw concentrations, a converted index, or some other metric.
The CPCB publishes formulas to convert concentrations into AQI values, but the report does not clarify whether this was applied. This ambiguity makes it impossible to evaluate the claimed reductions accurately.
2. No Time Frame Specified
The IIT-K report also fails to mention the averaging period for its measurements. Were the reported values based on hourly averages, 15-minute intervals, or 24-hour rolling averages? This matters because air quality in Delhi varies significantly throughout the day. Without this detail, there is no way to determine how long any improvement lasted, or whether the numbers are comparable across days.
3. Ignoring Natural Daily Air Quality Patterns
Perhaps the most significant flaw is the report’s failure to account for Delhi’s natural diurnal air quality cycle. The cloud seeding was conducted between roughly 2 p.m. and 5 p.m., precisely the time when Delhi’s air naturally improves in the afternoon.
While the report cites three locations, air quality monitoring data exists from at least 20 sites across the city. IIT Kanpur’s director, Manindra Agarwal, stated that 15 stations were operational but did not provide details about their location or type.
At least one of the cited locations, Burari Crossing, is monitored by the India Meteorological Department (IMD). Historical hourly data shows a consistent pattern: air quality improves between noon and 6 p.m., then deteriorates in the evening and night. A comparison of PM2.5 and PM10 levels on October 28 with the preceding three days confirms that the 2–5 p.m. window is naturally the cleanest part of the day, independent of any cloud seeding activity.
4. A Citywide Afternoon Improvement
This afternoon improvement is not unique to Burari Crossing. Monitoring stations across Delhi show similar trends. As temperatures rise during the day, vertical mixing in the atmosphere increases, dispersing pollutants and temporarily improving air quality.
“Air quality naturally improves in the afternoon because it is the warmest part of the day. In winter-like conditions, higher temperatures prevent pollutants from accumulating close to the ground,” explained environmental experts.
Because IIT Kanpur did not compare data against these natural daily variations, the reported reductions in PM2.5 and PM10 cannot be reliably attributed to cloud seeding.
5. Issues with Rainfall Data
The report also claims success based on rainfall data, citing Windy.com, a weather website that displays modelled estimates rather than measured precipitation. Reliance on modelled rather than observed rainfall further undermines the report’s credibility.
Why This Matters
Cloud seeding as a tool to reduce particulate pollution is still scientifically contentious, and rigorous testing is required to draw meaningful conclusions. This includes:
- Using well-calibrated air quality sensors with clear units and averaging periods.
- Comparing experimental days with control days, accounting for meteorological conditions like wind speed, temperature, and humidity.
- Analyzing data from multiple locations citywide.
- Ensuring rainfall measurements are ground-truth data, not estimates.
Without these methodological safeguards, any claims of success are premature and potentially misleading.
Conclusion
The IIT Kanpur report shared by the Delhi government is not a scientifically sound assessment of cloud seeding. By ignoring natural diurnal patterns, omitting units, failing to define measurement intervals, and relying on modelled rainfall data, the report gives the illusion of improvement where none can be definitively established.
As experts have emphasized, true evaluation of air quality interventions requires careful, statistically robust studies, not simple pre- and post-observation comparisons during naturally cleaner periods of the day.
In its current form, the IIT-K report cannot be used to claim that cloud seeding reduced particulate pollution in Delhi, and its conclusions should be treated with caution.
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