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Associations between fine particulate matter (PM2.5) exposure concentrations and a wide variety of undesirable outcomes, from autism and auto theft to elderly mortality, suicide, and violent crime, have been widely reported. Influential articles have argued that reducing National Ambient Air Quality Standards for PM2.5 is desirable to reduce these outcomes. Yet, other studies have found that reducing black smoke and other particulate matter by as much as 70% and dozens of micrograms per cubic meter has not detectably affected all-cause mortality rates even after decades, despite strong, statistically significant positive exposure concentration-response (C-R) associations between them. This paper examines whether this disconnect between association and causation might be explained in part by ignored estimation errors in estimated exposure concentrations. We use EPA air quality monitor data from the Los Angeles area of California to examine the shapes of estimated C-R functions for PM2.5 when the true C-R functions are assumed to be step functions with well-defined response thresholds. The estimated C-R functions mistakenly show risk as smoothly increasing with concentrations even well below the response thresholds, thus incorrectly predicting substantial risk reductions from reductions in concentrations that do not affect health risks. We conclude that ignored estimation errors obscure the shapes of true C-R functions, including possible thresholds, possibly leading to unrealistic predictions of the changes in risk caused by changing exposures. Instead of estimating improvements in public health per unit reduction (e.g., per 10 μg/m3 decrease) in average PM2.5 concentrations, it may be essential to consider how interventions change the distributions of exposure concentrations.Recent literature has identified associations between concentrations of fine particulate matter (PM2.5) in ambient air and adverse health effects at estimated exposure concentrations below current air ambient quality standards.The models and analyses used do not usually quantify uncertainty about exposure estimates.Realistic errors in exposure estimates can be estimated from available air quality monitoring stations by predicting concentrations at each station from concentrations at neighboring stations.Exposure estimation errors are large enough to obscure low-dose nonlinearities or thresholds in the true concentration-response (C-R) function, artificially making them appear to be linear at low doses.Better estimates of C-R functions will require modeling exposure distributions and estimation errors, rather than just estimating average exposure concentrations.