Katya Garg | Sustainable Development Professional

Air quality is one of the most visible urban crises of our time. Those of us living in cities feel it every winter, see it in the haze, and track it obsessively through AQI numbers that often cross alarming thresholds.

In regions like the NCR, AQI levels regularly exceed 400. Monitoring infrastructure exists. Sensors are deployed. Data is collected in real time.

And yet, the question that matters most often goes unanswered:

What happens after the data is collected?

When Monitoring Becomes the End Goal

During my work on building air-quality leadership in the NCR, in close collaboration with state government authorities, I observed a pattern that is far too common in urban environmental governance.

Significant effort goes into monitoring air quality. Dashboards are updated. Alerts are issued. Policies like GRAP are enabled and disabled based on readings.

But monitoring, by itself, is not management.

If data only triggers emergency responses without feeding into long-term, location-specific action, then we are reacting to pollution, not preventing it.

A city cannot be managed only by switching controls on and off.

Hotspots Are Identified. But Then What?

Hotspot identification has become a popular intervention. Maps are created. Vulnerable zones are highlighted.

But identification is only the first step.

Without a clear pathway from data → interpretation → decision → enforcement, hotspot mapping risks becoming a static exercise, informative, but ineffective.

Data has power only when it is translated into actionable decisions and supported by strategic planning that enables enforcement on the ground.

A Ground-Level Reality Check from Gurugram

While studying vulnerable air-quality hotspots in Gurugram, I deployed air-quality monitoring sensors across five different locations in the city.

What emerged was not a single problem, but multiple, highly localized realities.

In some areas, AQI spikes were driven by high dust concentration
In others, vehicular emissions dominated
Elsewhere, industrial activity was the primary contributor

The data clearly showed that each area required a different solution.

Yet, despite this clarity, no differentiated action followed.

The same broad measures were applied everywhere, regardless of cause, context, or feasibility.

This is where the gap becomes undeniable, not in data availability, but in decision-making.

Are We Under-Utilising Our Own Expertise?

This raises an uncomfortable but necessary question:

Are sustainability professionals being deployed primarily as data collectors rather than as strategic decision-makers?

In a domain that demands systems thinking, behavioural understanding, and contextual intelligence, roles often stop at assessment and reporting.

But sustainable development cannot succeed if those closest to ground realities are excluded from shaping strategy.

When Automation Exists, Why Is Strategy Missing?

Today, data collection and analysis can be automated. AI can reduce the burden of monitoring, pattern recognition, and reporting.

If technology can handle the repetitive tasks, then human effort should move upward, to strategy, synthesis, and implementation.

And yet, we rarely see decisions that integrate:

Real-time data
Human behaviour and compliance patterns
On-ground experience
Regional socio-economic context

This disconnect is where progress stalls.

The Shift We Need

Air quality challenges will not be solved by more sensors alone.

They require:

Data that informs action, not just awareness
Decentralised, context-specific interventions
Clear ownership of implementation
A shift in sustainability roles from observers to decision-makers

Until we treat implementation as seriously as data collection, cities will continue to measure their problems instead of solving them.

The real challenge is not knowing how bad the air is.

It is deciding, clearly, strategically, and contextually, what to do about it next.