The paper addresses the issue of exposure measurement error in time-series studies of air pollution and health, particularly focusing on the interpretation of epidemiologic studies on air pollution. Exposure measurement error is a well-recognized limitation in epidemiologic studies, especially when exposures occur over time and in multiple locations. The authors develop a systematic conceptual framework to understand the problem of measurement error and its consequences in time-series studies of particulate air pollution and mortality. They distinguish between two types of measurement errors: Berkson errors, where the average exposure within each stratum equals the true exposure, and classical errors, where the average exposure within each stratum equals the measured exposure. The framework is applied to a log-linear regression model used in time-series studies, and the authors propose a conceptual framework to evaluate the effects of measurement errors in such models. They also present a simple analysis using data from the Particle Total Exposure Assessment Methodology (PTEAM) Study to illustrate the magnitude of measurement error effects. The paper concludes with a discussion of the implications of measurement error for interpreting epidemiologic studies on air pollution and suggests additional data needed to address open questions.The paper addresses the issue of exposure measurement error in time-series studies of air pollution and health, particularly focusing on the interpretation of epidemiologic studies on air pollution. Exposure measurement error is a well-recognized limitation in epidemiologic studies, especially when exposures occur over time and in multiple locations. The authors develop a systematic conceptual framework to understand the problem of measurement error and its consequences in time-series studies of particulate air pollution and mortality. They distinguish between two types of measurement errors: Berkson errors, where the average exposure within each stratum equals the true exposure, and classical errors, where the average exposure within each stratum equals the measured exposure. The framework is applied to a log-linear regression model used in time-series studies, and the authors propose a conceptual framework to evaluate the effects of measurement errors in such models. They also present a simple analysis using data from the Particle Total Exposure Assessment Methodology (PTEAM) Study to illustrate the magnitude of measurement error effects. The paper concludes with a discussion of the implications of measurement error for interpreting epidemiologic studies on air pollution and suggests additional data needed to address open questions.