The Averaged City: How 38 Years of Observations in India Reveal Systematic Biases in Air Quality Reports and Blind Spots in Global Urban Governance

When a city is defined by a single number

Globally, approximately 8.7 million people die prematurely each year due to air pollution—a figure comparable to malnutrition and far exceeding malaria and AIDS. Yet, while the scientific community continues to focus on emission sources, atmospheric chemistry, and health burdens, a more insidious issue is quietly reshaping the logic of urban governance: the very way we measure, calculate, and publicize air pollution information may itself be a source of systematic bias.

India, as one of the most polluted and fastest-urbanizing countries in the world, provides an extreme observational case. A study recently published in Scientific Reports, based on 38 years of continuous monitoring data from 64 cities between 1987 and 2024, paints an unsettling long-term picture: air quality has continued to deteriorate, with pollution spreading from the traditional Indus-Ganges Plain to southern and coastal regions; particulate matter dominates in more than 85% of observations, sulfur dioxide impacts have fallen by 95%, while nitrogen dioxide concentrations have doubled. But the core finding most worthy of global cities' attention is not pollution itself, but a virtually unexamined link in urban air quality reporting systems—the averaging method used for city-level AQI.

The tyranny of the average: how spatial differences are smoothed away

Since India launched its National Air Quality Index (NAQI) in 2015, city-level air quality reporting has relied on a simple and transparent rule: take the arithmetic mean of AQI values from individual monitoring stations to obtain an overall city figure. The implicit premise of this method is that each monitoring station equally represents the real exposure of urban residents. But the reality is clearly not so.

In metropolitan areas like the National Capital Region of Delhi, which spans over 30,000 square kilometers and has more than 80 monitoring stations, a single aggregate value compresses the high pollution of industrial zones, traffic emissions of commercial areas, the relative cleanliness of residential neighborhoods, and the differences at the urban periphery into one stark number. Research shows that this traditional averaging method can misreport true exposure levels by as much as 10 percentage points. In other words, a city may be reported as "moderately polluted" while in reality a significant proportion of residents are exposed to "severe pollution"—and vice versa.

This is not a mere technical flaw but an institutional exposure bias. The distribution of monitoring stations itself tends to favor industrial and residential areas, while commercial zones, sensitive areas, and urban-rural fringes are underrepresented. This spatial imbalance, combined with the homogenizing effect of the averaging method, makes air quality reports at best a rough approximation, and at worst a tool for concealing environmental inequality.

The structural implications of long-term trends## Structural Implications of Long-Term Trends

The 38 years of data not only reveal the temporal evolution of pollution, but also expose deep-seated problems in India's urban development model. In 1987, days with levels ranging from Poor to Severe accounted for only 15%, but by 2024, this proportion had risen to over 35%. The geographic expansion of pollution clearly shows that environmental degradation is spreading from traditional heavy-industry and densely populated areas to the southern and coastal cities that were previously relatively clean. This corresponds precisely to the industrial diffusion, urban sprawl, and growth in energy demand that followed India's economic liberalization.

Notably, there has been a structural transformation of pollutants: the impact of sulfur dioxide has plummeted by 95%, related to coal desulfurization policies and changes in industrial structure; while nitrogen dioxide has doubled, pointing to increasing motor vehicle traffic and urban traffic density. These changes mean that the focus of urban air quality governance must shift from traditional industrial point-source control to more complex management of urban spatial transport and energy systems.

Information Architecture as a Governance Tool

Air pollution data are not merely scientific measurements; they constitute a public information infrastructure. The AQI designs of different countries reflect their respective governance philosophies: the U.S. EPA's AQI is oriented toward health protection; Canada uses an Air Quality Health Index with health risk classification; and China has established national standards covering multiple pollutants. However, all systems face the same core question: how do the index architecture and the spatial distribution of monitoring networks affect their representativeness and equity?

The Indian case shows that many current urban reporting systems remain stuck in a "technocentric" stage—believing that the data themselves can convey the truth, while ignoring the selection bias inherent in data collection and aggregation. The research team consulted 450 stakeholders from 23 states and found strong support for exposure-based air quality reporting. This suggests that the public is not satisfied with a simple number, but yearns for information that matches their actual breathing experience.

Exposure Weighting: A New Urban Governance Paradigm

The exposure-weighted framework proposed by the study is, in essence, a re-politicization of the current reporting system—it refuses to treat the city as a homogeneous space, instead incorporating the actual distribution and activity patterns of residents into calculations, thereby correcting spatial representativeness problems. This framework enhances the fidelity and transparency of reporting and can be extended to other cities globally.

The significance of this shift goes far beyond technical improvement. It reflects the evolution of urban governance from "averaging" to "spatial justice." In an increasingly divided city, the world of averages is fictitious; only assessment based on real exposure can provide a reliable foundation for policy intervention, and enable urban residents to trust public information and take protective actions.

Information Challenges for Cities in the Global SouthIndia's 38 years of observations remind us that the quality of urban development depends not only on skyscrapers and metro networks, but also on whether a city can honestly confront its own air. A city that integrates exposure bias into its institutional design is one that truly possesses the strategic foundation for moving toward a sustainable future.

Conclusion: Redefining the Future of Urban Environmental Governance

This study may appear to focus on India, but in fact it touches a universal nerve in global urban governance. As our world becomes increasingly data-driven, the way data itself is constructed becomes a form of power. The homogenization bias in urban air quality reporting is a microcosmic slice of how this power operates.

Establishing an exposure-weighting mechanism is not merely a correction to measurement technology, but a reshaping of urban governance philosophy—placing residents' real lives at the core of policy, acknowledging the differences and inequalities within cities, and confronting these realities in a transparent way. For Global South countries in the midst of accelerated urbanization, this may be an institutional reform more urgent than building more monitoring stations.

Urban competition in the future will be not only a contest of economic output and infrastructure, but also a competition of governance quality, information transparency, and environmental justice. India's 38 years of data provide a starting point worthy of profound global reflection.