In the narrow lanes of Dharavi, the air has a texture. It clings to the skin—a mix of recycled plastic fumes, sweat, and the metallic tang from thousands of small-scale welding units. For the families who live and work here, this air is the backdrop of daily life, a sensory constant as familiar as the call to prayer. But for a researcher arriving with a portable air quality monitor, that same air is a data point, a concentration of PM2.5 to be measured, analyzed, and published. This duality—the lived environment versus the quantified environment—is the central tension of environmental health equity in Indian cities. It is a field that demands we be as fluent in the language of municipal budgets and epidemiological models as we are in the unspoken hierarchies of a basti. To do this work honestly, we must navigate not just the science of exposure, but the politics of whose knowledge counts.
Environmental health research in urban India is never a purely technical exercise. It is an act of translation between the formal world of peer-reviewed journals and the informal world where most environmental suffering unfolds. The challenge is not simply to collect better data, but to understand how data becomes evidence, how evidence becomes policy, and how policy is often shaped by forces that have little to do with the numbers on a page.
The Dual Register of Environmental Health Data
Walk through any industrial neighborhood in Mumbai, and you will encounter two parallel realities. The first is the official reality: ambient air quality monitors mounted on rooftops, measuring regional particulate matter to assess city-wide compliance with national standards. The second is the lived reality: a waste-picker sorting through mixed debris, inhaling a concentrated cocktail of toxins that no ambient monitor will ever capture. Both realities are true, but they operate on different scales and serve different masters.
This is the fundamental tension in our work. Scientific rigor demands standardized methods, validated instruments, and statistically representative samples. Without these, our findings will be dismissed by policymakers and academic journals alike. Yet the very act of standardization can erase the granular, place-based knowledge that makes environmental health inequities visible in the first place. A city-wide average for nitrogen dioxide might show a marginal decline, masking the fact that levels near a newly widened road in a low-income neighborhood have tripled.
The politics enter through this gap. When a municipal corporation presents a clean air action plan, it relies on data from its sparse network of regulatory monitors. These monitors are often placed away from the most polluted micro-environments—the traffic junctions where street vendors spend twelve-hour days, the cramped alleys where informal e-waste recycling happens behind closed doors. The resulting data is scientifically valid but politically convenient. It allows a city to report progress without confronting the uncomfortable truth that the burden of pollution is distributed with savage inequality.
When Communities Become Citizen Scientists
In response, a quiet revolution has been unfolding across Indian cities. Community-based organizations, often led by women, are beginning to document their own environmental exposures. In Shivaji Nagar, near Mumbai’s Deonar dumping ground, a group of waste-picker women partnered with a local nonprofit to wear personal air samplers during their shifts. The data they collected told a story that no regulatory monitor could: peak exposures during the manual sorting of mixed waste were orders of magnitude higher than the already-elevated background levels.
This is community-based participatory research (CBPR) in its most urgent form. It is not merely a data-gathering exercise; it is a political act. When residents of an informal settlement map the locations of open drains, illegal factories, and uncollected waste, they are producing what scholars call “counter-data”—information that challenges official narratives. This data is often messy by academic standards. The sampling may not be random, the instruments may be low-cost and less precise, and the documentation may rely on oral histories rather than calibrated logs. But its power lies in its specificity and its ownership. The community that collects the data is also the community that can use it to demand change.
However, this approach creates a new set of tensions. Municipal officials often dismiss community-collected data as anecdotal or biased. Public health researchers worry about the validity of low-cost sensors, which can drift in high-humidity conditions or cross-react with other pollutants. The very act of equipping communities with these tools can be seen as a political provocation, especially when the findings implicate powerful local industries. I have sat in meetings where a meticulously documented community map of illegal kilns was dismissed by a planning official as “just a drawing,” while a less accurate but officially sanctioned satellite map was treated as definitive truth.
The Infrastructure of Credibility
This brings us to a hard question: What makes environmental health data credible, and to whom? For a journal editor, credibility rests on methodology, peer review, and institutional affiliation. For a community organizer, credibility rests on lived experience, transparency, and the trustworthiness of the people who collected the data. For a municipal engineer, credibility may depend on whether the data aligns with existing infrastructure plans and political priorities. These different standards of credibility are not just academic; they determine which problems get addressed and which communities remain invisible.
Consider the case of heat stress among outdoor workers. A peer-reviewed study using personal heat monitors and validated physiological measures might find that construction workers in Ahmedabad experience dangerous core body temperatures during summer afternoons. This study, published in a reputable journal, could influence national occupational health guidelines. Meanwhile, a community group in the same city might conduct a participatory survey where workers describe symptoms of heat exhaustion, map the locations of water stations, and document the times of day when work becomes unbearable. This data, though less “rigorous” by academic standards, might be far more effective at mobilizing local action and pressuring contractors to change work schedules.
The most effective environmental health research, I have found, refuses to choose between these registers. It treats community knowledge not as a supplement to scientific data, but as a co-equal form of evidence that asks different questions and demands different kinds of accountability. This is not a call for lowering standards; it is a call for expanding our definition of what counts as a valid observation about the world.
The Political Economy of Research Agendas
We must also be honest about who funds environmental health research and what questions they are willing to ask. In India, a significant portion of environmental health funding comes from international development agencies, corporate social responsibility (CSR) initiatives, and government bodies with a stake in the outcomes. This funding landscape shapes the research agenda in subtle but profound ways. A study on the health impacts of vehicular emissions is more likely to be funded than a study on the occupational exposures of informal recyclers, because the former aligns with the priorities of middle-class environmentalism and the latter does not.
This is not a conspiracy; it is a structural bias. Funders want actionable results that can inform policy or technology solutions. The health of informal workers—who are often migrants, lacking political representation, and working in sectors that exist in a legal gray area—is a problem without an easy policy fix. It is easier to fund a project that installs air purifiers in schools than one that documents the respiratory health of waste-pickers and demands that the municipality provide them with protective equipment and formal recognition.
Researchers who work at the intersection of environmental health and urban poverty must therefore become adept at navigating this political economy. We learn to frame our questions in ways that attract funding without distorting the reality on the ground. We build coalitions with civil society organizations that can provide access to communities and help translate findings into advocacy. We publish in journals that value methodological innovation, while also writing policy briefs and op-eds that reach a wider audience. It is a delicate dance, and sometimes we stumble.
The Limits of Exposure Science
Even when research is well-funded and methodologically sound, it runs up against the limits of what exposure science can tell us. Measuring an individual’s exposure to a single pollutant over a 24-hour period is technically challenging and expensive. Doing so for a population, across multiple pollutants, over a lifetime, while accounting for confounding factors like nutrition, stress, and access to healthcare, is nearly impossible. We rely on models, proxies, and assumptions that inevitably simplify a complex reality.
This simplification has political consequences. When we reduce environmental health to a set of measurable exposures, we risk ignoring the structural factors that produce those exposures. A child living near a waste dump is not just exposed to higher levels of particulate matter; she is also more likely to be malnourished, to have limited access to clean water, and to live in a household with precarious income. These factors interact in ways that our models struggle to capture. Focusing narrowly on air quality, without addressing the broader determinants of health, can lead to interventions that are technically sound but practically useless—like distributing masks to families who cannot afford to replace the filters.
This is where the concept of structural violence becomes essential. The term, coined by peace researcher Johan Galtung and later expanded by medical anthropologist Paul Farmer, describes how social structures—economic, political, legal, and cultural—can cause harm to individuals by preventing them from meeting their basic needs. In the context of urban environmental health, structural violence is the reason that certain neighborhoods become sacrifice zones for polluting industries, that informal workers are excluded from occupational health regulations, and that the voices of affected communities are systematically excluded from decision-making processes.
Building a Practice of Engaged Research
So how do we, as researchers and practitioners, navigate this terrain without becoming either cynical or naive? The answer, I believe, lies in cultivating a practice of engaged research that is both rigorous and reflexive. This means acknowledging the political dimensions of our work from the outset, rather than pretending to a neutrality that does not exist. It means building long-term relationships with communities, not just extracting data from them. And it means being willing to use our institutional power to amplify community knowledge, even when that knowledge challenges official narratives.
One model I have seen work well is the community-based air quality monitoring network. In several Indian cities, groups of residents, often led by women, use low-cost sensors to measure hyperlocal air pollution. The data is publicly accessible and is used to advocate for specific interventions: rerouting truck traffic, enforcing emission controls on local industries, or creating green buffers. The scientific limitations of the sensors are openly acknowledged, but the data’s political power comes from its granularity and its connection to lived experience. When a mother can show that the air outside her child’s school is five times worse than the city’s official reading, it changes the terms of the debate.
Another promising approach is the use of citizen juries or community advisory boards to set research priorities. Instead of researchers deciding what to study, communities identify the environmental health issues that matter most to them. This not only ensures that research is relevant, but also builds community capacity to engage with scientific evidence and hold authorities accountable. In a pilot project in Pune, a community advisory board identified indoor air pollution from cooking fuels as a higher priority than outdoor air pollution, redirecting research efforts toward an issue that had been largely invisible to city-level policymakers.
The Ethics of Visibility
There is also an ethical dimension to this work that is rarely discussed in methods sections. When we document environmental health hazards in a community, we make that community visible in new ways—to policymakers, to the media, to potential allies. But visibility can be a double-edged sword. It can attract resources and political attention, but it can also attract enforcement actions that harm the very people we seek to help. If we document that a settlement is located on a floodplain with high levels of water contamination, the municipal response might not be to improve drainage and water supply, but to demolish the settlement as “illegal” and displace its residents.
This is not a hypothetical concern. In Delhi, environmental health data has been used to justify the demolition of “non-conforming” industries and the eviction of informal workers, with little provision for alternative livelihoods. Researchers must therefore think carefully about the potential consequences of their work and engage with communities about how data should be used and who should control it. The principle of data sovereignty—the right of communities to own and control data about themselves—is as important in environmental health as it is in other domains.
Toward a Politically Literate Environmental Health Practice
What would it mean for environmental health research to be truly politically literate? It would mean, first, recognizing that all data is produced within a political context and that the choice of what to measure, how to measure it, and how to interpret the results is never neutral. It would mean building partnerships with communities that go beyond informed consent to genuine collaboration and shared decision-making. It would mean being transparent about the limitations of our methods and the uncertainties in our findings, while still using those findings to advocate for change.
It would also mean engaging with the messy, contentious world of urban governance. Environmental health researchers cannot afford to remain in the laboratory or the academic journal. We must be present in the rooms where decisions are made—the municipal planning meetings, the public hearings, the budget consultations—and we must be prepared to translate our findings into the language of policy and politics. This is uncomfortable work for many of us, trained as we are to value objectivity and distance. But distance is a luxury that communities living with environmental hazards cannot afford.
In my own practice, I have learned to see my role not as an expert who delivers answers, but as a facilitator who helps communities ask better questions. The most important data we collect is not always the concentration of a pollutant, but the documentation of a process: how a decision was made, who was consulted, what alternatives were considered. This kind of process data can be more powerful than any epidemiological study in holding authorities accountable.
Frequently Asked Questions
Why is community-collected environmental data often dismissed by officials?
Community-collected data is frequently dismissed because it may not follow standardized scientific protocols, uses lower-cost instruments, and is perceived as advocacy rather than objective research. However, this dismissal often masks a political discomfort: community data frequently challenges official narratives and exposes gaps in regulatory monitoring. The credibility of data should be assessed based on its fitness for purpose, not just its adherence to laboratory standards. When the goal is to understand hyperlocal exposures that official monitors miss, community-based methods can be more valid than distant regulatory sensors.
How can researchers avoid causing harm to the communities they study?
Researchers can minimize harm by practicing data sovereignty—ensuring communities own and control the data collected about them. This means involving community members in decisions about what to study, how to collect data, and how findings will be used. It also requires a careful analysis of potential negative consequences, such as eviction or loss of livelihood, before publishing sensitive information. Building long-term, trust-based relationships rather than extractive, one-time studies is essential. Finally, researchers should be prepared to advocate for the community’s interests, even when it is politically inconvenient.
What is the difference between environmental health equity and environmental justice?
While the terms are often used interchangeably, environmental health equity focuses specifically on the fair distribution of health outcomes related to environmental conditions, emphasizing measurable disparities in exposure and disease. Environmental justice is a broader social movement and legal framework that addresses the disproportionate environmental burdens borne by marginalized communities, including issues of procedural fairness, recognition, and participation in decision-making. In practice, environmental health equity research provides the evidence base that environmental justice movements use to demand change. Both are necessary: equity without justice can become a technocratic exercise, while justice without equity can lack the data needed to prove harm.
Can low-cost air quality sensors be trusted for research?
Low-cost sensors have known limitations: they can drift over time, are sensitive to humidity and temperature, and may cross-react with multiple pollutants. However, when properly calibrated against reference instruments and deployed in networks, they can provide valuable data on spatial and temporal patterns that fixed regulatory monitors miss. The key is to be transparent about their limitations and to use them for the questions they are best suited to answer—such as identifying pollution hotspots, comparing relative levels across locations, and engaging communities in the research process. They are not a replacement for regulatory-grade monitors, but a complement that fills critical data gaps in under-monitored areas.