The Environmental Protection Agency’s (EPA’s) recent decision to stop monetizing the health benefits of reducing fine particulate matter, known as PM2.5, has refocused attention on a long-running debate over air pollution regulation. The agency announced that it will temporarily suspend assigning dollar values to the health benefits of PM2.5 and ozone reductions while it reassesses how to incorporate uncertainty in those estimates into regulatory analysis. Environmental activists and many epidemiologists argue that PM2.5 is a leading contributor to premature death, and projected reductions in these particles account for an overwhelming share of the estimated benefits of federal environmental regulation. Yet substantial disagreement remains over how confident we should be in estimates of PM2.5’s health effects, and how much regulatory weight those estimates should carry.

PM2.5 is a regulatory category referring to airborne particles and droplets less than 2.5 micrometers in diameter, or smaller than a human hair. Emitted from a wide range of natural and man-made sources, including vehicles, factories, power plants, restaurants, wildfires, and wind-blown dust, these particles are small enough to penetrate deep into the lungs and potentially the bloodstream. Exposure may aggravate short-term health conditions such as asthma and may contribute to long-term, chronic health problems. Many epidemiologists and environmental advocates contend that long-term exposure substantially increases mortality risk.

Those claims matter enormously for public policy. According to the Office of Management and Budget, from 2006 to 2016 regulations issued by the Environmental Protection Agency accounted for at least 71 percent of the total monetized benefits and 55 percent of the total monetized costs of all major federal regulations. Within EPA regulations, air quality rules accounted for roughly 95 percent of total estimated benefits, and the majority of those benefits stemmed from projected reductions in PM2.5.

Importantly, many of these PM2.5 benefits arise not from regulations explicitly targeting fine particulate matter, but as secondary or “co-benefits” of rules aimed at other pollutants. A prominent example is the Mercury and Air Toxics Standards (MATS) rule, for which the EPA estimated that roughly 99 percent of the regulation’s monetized benefits came not from reductions in mercury or hazardous air toxics, the rule’s stated purpose, but from ancillary reductions in PM2.5. In such cases, PM2.5 co-benefits effectively determine whether a regulation appears economically justified at all.

Given these stakes, the underlying evidence deserves careful scrutiny. The central scientific question is relatively straightforward: does long-term exposure to fine particulate matter cause premature death? However, establishing both the existence and magnitude of this causal relationship is incredibly complicated.

In an ideal world, researchers would rely on randomized controlled trials, the gold standard for causal inference, in which individuals are randomly assigned to different levels of pollution exposure. In practice, logistical constraints make such experiments impossible. As a result, epidemiologists rely on observational studies that use statistical methods to analyze naturally occurring variation in pollution exposure and health outcomes.

For decades, these studies have reported statistical associations between air pollution and mortality, but the findings have been subject to sustained scrutiny. Critics argue that many studies fail to adequately account for confounding factors, such as occupational history, preexisting health conditions, physical activity, socioeconomic status, and diet, that are correlated with both pollution exposure and mortality risk. When these factors are imperfectly measured or omitted, they can create the appearance of a causal relationship even where none exists. 

Additional concerns stem from exposure measurement error and selection effects, including migration and residential sorting. Individuals with different health risks may systematically sort into areas with different pollution levels, biasing estimates in ways that are difficult to detect or correct. Measurement error in pollution exposure further complicates efforts to draw precise conclusions. 

>>>READ: PM2.5, Regulatory Uncertainty, and the Role of Science in Policymaking

Proponents of more stringent air quality standards point to newer studies using more sophisticated statistical techniques as evidence that long-term PM2.5 exposure causes mortality. These studies, however, do not escape some of the fundamental limitations of observational data. The debate ultimately turns on whether these shortcomings reflect isolated imperfections or more systemic limitations of air pollution epidemiology.

The EPA’s recent decision represents an attempt to better incorporate these sources of uncertainty into the regulatory process. Acknowledging uncertainty does not mean denying the potential for air pollution to harm human health. Rather, it requires reassessing how, given that uncertainty, the risks of air pollution exposure should be weighed against other regulatory considerations. The EPA’s historical tendency to downplay epidemiological uncertainty has often conveyed a false sense of precision to policymakers and the public. If the agency follows through on its stated intent, the current move may constitute a warranted corrective action. 

PM2.5 plays an outsized role in the modern regulatory state, and getting its mortality effects wrong carries serious consequences. Overstating health benefits risks justifying an expansive—and expensive—array of regulations from vehicle emissions to household appliance efficiency standards. Understating those benefits, on the other hand, risks allowing preventable harms to human health to persist. The EPA’s recent decision offers an opportunity to step back and reassess how PM2.5 health benefits are estimated, communicated, and used in regulatory decision-making, and to do so in a way that more honestly reflects the limits of the underlying science.

The views and opinions expressed are those of the author’s and do not necessarily reflect the official policy or position of C3.